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89 results on '"Emmanuel Mazer"'

1. Cognitive Computation

Author: Raphaël Laurent, Mohamad Othman Abdallah, Jorge Lobo, Emmanuel Mazer, Marvin Faix, and Ronan Le Hy
Subjects: Computer science, business.industry, Computation, Cognition, 02 engineering and technology, Machine learning, computer.software_genre, Bayesian inference, 020202 computer hardware & architecture, Bayesian statistics, Relevance vector machine, Frequentist inference, 0202 electrical engineering, electronic engineering, information engineering, Fiducial inference, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract: Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.
Published: 2017
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2. Cognitive Computation

Author: Marvin Faix, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, and Jorge Lobo
Subjects: 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, 020202 computer hardware & architecture
Abstract: Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.
Published: 2020
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3. An n-Bit Adder Realized via Coherent Optical Parallel Computing

Author: Maurizio Coden, Carlo Nazareno Dibenedetto, Hugo Gattuso, Marinella Striccoli, Noam Gross, Ariela Donval, Barbara Fresch, Françoise Remacle, Elisabetta Collini, Yossi Paltiel, Bogdan Reznychenko, and Emmanuel Mazer
Subjects: Adder, Computer science, Computation, 02 engineering and technology, Parallel computing, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, Nonlinear system, Superposition principle, Quantum state, SIMD, 0210 nano-technology, Coherent spectroscopy, Quantum
Abstract: The quantum properties of nanosystems present a new opportunity to enhance the power of classical computers, both for the parallelism of the computation and the speed of the optical operations. In this paper we present the COPAC project aiming at development of a ground-breaking nonlinear coherent spectroscopy combining optical addressing and spatially macroscopically resolved optical readout. The discrete structure of transitions between quantum levels provides a basis for implementation of logic functions even at room temperature. Exploiting the superposition of quantum states gives rise to the possibility of parallel computation by encoding different input values into transition frequencies. As an example of parallel single instruction multiple data calculation by a device developed during the COPAC project, we present a n-bit adder, showing that due to the properties of the system, the delay of this fundamental circuit can be reduced.
Published: 2019
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4. Bayesian time-domain multiple sound source localization for a stochastic machine

Author: Emmanuel Mazer, Laurent Girin, Marvin Faix, Raphael Frisch, Jacques Droulez, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CRISSP (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Signal processing, Computer science, Bayesian probability, 020206 networking & telecommunications, Statistical model, specific hardware, 02 engineering and technology, Acoustic source localization, Bayesian stochastic machine, time-domain processing, Bayesian inference, Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiple sound source localization, Time domain, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract: We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.
Published: 2019
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5. A cognitive stochastic machine based on Bayesian inference: a behavioral analysis

Author: Marvin Faix, Laurent Fesquet, Augustin Lux, Raphael Frisch, Emmanuel Mazer, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)
Subjects: Optimal design, [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Computation, Bayesian probability, Probabilistic logic, 02 engineering and technology, Sensor fusion, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0305 other medical science, Algorithm
Abstract: International audience; Bayesian models and stochastic computing form a promising paradigm for non-conventional, bio-inspired computation architectures. In particular, they are able to handle uncertainty and promise low power consumption. In this paper we study the application of such an architecture, the Sliced Bayesian Machine (SlicedBM) to a real-world problem, Sound Source Localization (SSL) for robots. We present an analysis of the quality of results and of computing time according to several parameters: sensor precision, result threshold, internal word length. Furthermore, we show that sensor data precision does not heavily influence the computation. On the opposite, the precision of the probability values plays an important role on result quality. This parameter also determines the circuit size. We also show that the higher the re-sampling threshold (RT), the better the distribution computed by the machine. Our results make it possible to choose optimal design parameters for a circuit along several trade-offs, and according to a given sensor fusion application.
Published: 2018

6. A Bayesian stochastic machine for sound source localization

Author: Laurent Girin, Raphaël Laurent, Pierre Bessière, Laurent Fesquet, Raphael Frisch, Emmanuel Mazer, Augustin Lux, Jacques Droulez, Marvin Faix, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CRISSP (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interpretation and Modelling of Images and Videos (PERCEPTION), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Posterior probability, Bayesian probability, 02 engineering and technology, Acoustic source localization, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 0305 other medical science, Likelihood function, Algorithm
Abstract: International audience; Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called " time dilution problem " (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10³ in the computation.
Published: 2017

7. Bayesian Sensor Fusion with Fast and Low Power Stochastic Circuits

Author: Raphaël Laurent, Jorge Lobo, Jacques Droulez, Pierre Bessière, M. Awais Aslam, Emmanuel Mazer, Alexandre Coninx, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], and University of Coimbra
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, 020208 electrical & electronic engineering, Bayesian probability, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Energy consumption, Sensor fusion, Bayesian inference, 020202 computer hardware & architecture, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Hardware Architecture, Computer engineering, Probabilistic logic network, Logic gate, 0202 electrical engineering, electronic engineering, information engineering, Applications of artificial intelligence
Abstract: International audience; —As the physical limits of Moore's law are being reached, a research effort is launched to achieve further performance improvements by exploring computation paradigms departing from standard approaches. The BAMBI project (Bottom-up Approaches to Machines dedicated to Bayesian Inference) aims at developing hardware dedicated to probabilistic computation , which extends logic computation realised by boolean gates in current computer chips. Such probabilistic computing devices would allow to solve faster and at a lower energy cost a wide range of Artificial Intelligence applications, especially when decisions need to be taken from incomplete data in an uncertain environment. This paper describes an architecture where very simple operators compute on a time coding of probability values as stochastic signals. Simulation tests and a reconfigurable logic hardware implementation demonstrated the feasibility and performances of the proposed inference machine. Hardware results show this architecture can quickly solve Bayesian sensor fusion problems and is very efficient in terms of energy consumption.
Published: 2016

8. Evidences of Stochastic Bayesian Machines Robustness Against SEUs and SETs

Author: Raphaël Laurent, Alexandre Siqueira Guedes Coelho, Miguel Solinas, S. Karaoui, Raoul Velazco, Juan A. Fraire, Emmanuel Mazer, Nacer-Eddine Zergainoh, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Probayes [Montbonnot], Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Laboratoire d'Informatique de Grenoble (LIG )
Subjects: 010308 nuclear & particles physics, Stochastic process, Computer science, business.industry, Computation, Bayesian probability, Probabilistic logic, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, 020202 computer hardware & architecture, PACS 8542, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, Artificial intelligence, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, business, computer
Abstract: International audience; This work revisits the stochastic computationparadigm as a way to implement architectures dedicated toBayesian computation. It is assumed that Stochastic BayesianMachines (SMBs) are intrinsically tolerant to the effects ofradiation. However, practical assessment is mandatory beforeconsidering SBMs in hazardous environments. Results of faultinjectioncampaigns performed at the RTL level provide the firstevidences of SBMs robustness with respect to SEUs and SETs.
Published: 2016

9. Autonomous Robot Controller Using Bitwise GIBBS Sampling

Author: Raphaël Laurent, Emmanuel Mazer, Marvin Faix, Dominique Vaufreydaz, Rémi Canillas, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Computer science, Bayesian programming, 01 natural sciences, Computer Science::Robotics, symbols.namesake, Gibbs sampling, Control theory, 0103 physical sciences, Obstacle avoidance, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Bitwise operation, Stochastic computing, 010308 nuclear & particles physics, business.industry, Control engineering, Autonomous robot, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], autonomous robots, symbols, stochastic computing, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, Von Neumann architecture
Abstract: International audience; In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.
Published: 2016

10. Design of Stochastic Machines Dedicated to Approximate Bayesian inferences

Author: Jacques Droulez, Pierre Bessière, Raphaël Laurent, Marvin Faix, Emmanuel Mazer, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, Bayesian probability, Inference, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, 01 natural sciences, Bayesian Programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, Joint probability distribution, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Stochastic computing, 010308 nuclear & particles physics, 020206 networking & telecommunications, Computer Science Applications, Human-Computer Interaction, Bayesian statistics, symbols, stochastic computing, Stochastic optimization, Information Systems, Gibbs sampling, Von Neumann architecture
Abstract: International audience; We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture the prior knowledge on which the inference is built. The output of the machine is devised to continuously sample the joint probability distribution of interest. While the method is explained on a simple example, we show that our machine computes a good approximation of the solution to a problem intractable in exact inference.
Published: 2016
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11. Exact and approximate inference in ProBT

Author: Linda Smail, Pierre Bessière, Juan-Manuel Ahuactzin, Emmanuel Mazer, Kamel Mekhnacha, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: [SCCO.COMP]Cognitive science/Computer science, Inference, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Artificial Intelligence, Frequentist inference, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Point estimation, Inference engine, Rule of inference, ComputingMilieux_MISCELLANEOUS, Mathematics, 010308 nuclear & particles physics, business.industry, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Bayesian statistics, Approximate inference, Fiducial inference, 020201 artificial intelligence & image processing, Artificial intelligence, business, Algorithm, computer, Software
Abstract: We present a unifying framework for exact and approximate inference in Bayesian networks. This framework is used in "ProBT", a general purpose inference engine for probabilistic reasoning and incremental model construction. This paper is not intended to present ProB T but to describe its underlying algorithms mainly the "Successive Restrictions Algorithm " (SRA) for exact inference, and the "Monte Carlo Simultaneous Estimation and Maximization" (MCSEM) algorithm for approximate inference problems. The main idea of ProBT is to use "probability expressions " that can be "exact" or "approximate" as basic bricks to build more complex models incrementally.
Published: 2007
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12. Cognitive computation: A Bayesian machine case study

Author: Jorge Lobo, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, Emmanuel Mazer, and Marvin Faix
Subjects: Sequence, Theoretical computer science, business.industry, Computer science, Computation, Carry (arithmetic), Bayesian probability, Probabilistic logic, Toolchain, symbols.namesake, symbols, Noise (video), Artificial intelligence, business, Von Neumann architecture
Abstract: The work presented in this paper is part of the BAMBI project, which aims at better understanding natural cognition by designing non Von Neumann machines with biologicaly plausible hardware. Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. Our work on the automatic design of probabilistic machines computing soft inferences with an arithmetic based on stochastic bitstreams allowed us to develop the following compilation toolchain: given a high level description of some general problem (typically to infer some knowledge from a model given some observations), formalized as a Bayesian Program, our toolchain automatically builds a low level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic.We designed as a validating example a circuit description of a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications.
Published: 2015
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13. The design and implementation of a Bayesian CAD modeler for robotic applications

Author: Pierre Bessière, Kamel Mekhnacha, Emmanuel Mazer, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, Mathematical optimization, geometric constraints, Computer science, Generalization, Monte Carlo method, Bayesian probability, Bayesian reasoning, [SCCO.COMP]Cognitive science/Computer science, CAD, 02 engineering and technology, Bayesian inference, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, business.industry, Monte Carlo methods, Robotics, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Computer Science Applications, Human-Computer Interaction, Constraint (information theory), Hardware and Architecture, Control and Systems Engineering, Probability distribution, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software
Abstract: International audience; We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler.
Published: 2001
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14. Manipulation Planning for Redundant Robots: A Practical Approach

Author: Kamal Gupta, Emmanuel Mazer, and Juan-Manuel Ahuactzin
Subjects: 0209 industrial biotechnology, Applied Mathematics, Mechanical Engineering, Degrees of freedom, Motion (geometry), 02 engineering and technology, Set (abstract data type), 020901 industrial engineering & automation, Artificial Intelligence, Control theory, Modeling and Simulation, 0202 electrical engineering, electronic engineering, information engineering, Robot, 020201 artificial intelligence & image processing, Configuration space, Electrical and Electronic Engineering, Software, Mathematics
Abstract: An emerging paradigm in solving the classical motion- planning problem (among static obstacles) is to capture the connectivity of the configuration space using a finite (but pos sibly large) set of landmarks (or nodes) in it. In this paper, we extend this paradigm to manipulation-planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial configuration to a final configuration while avoiding collisions with the static obstacles and other movable objects in the environment. Our specific approach adapts Adriadne's clew algorithm, which has been shown effective for classical motion-planning prob lems (Mazer et al. 1994; Ahuactzin 1994). In our approach, landmarks are placed in lower dimensional submanifolds of the composite configuration space. These landmarks repre sent stable grasps that are reachable from the initial con figuration. From each new landmark, the planner attempts to reach the goal configuration by executing a local plan ner, again in a lower (but different) dimensional submani fold of the composite configuration space. The approach is probabilistically resolution complete, does not assume that a closed-form inverse-kinematics solution for the manipulator is available, and is particularly suitable for redundant manip ulators. We also demonstrate that our approach is practical for realistic problems in three-dimensional environments with manipulator arms having fairly large numbers of degrees of freedom. We have experimented with this approach for a 7- DOF manipulator in 3-D environments with one movable ob ject, and computation times range between a few minutes and a few tens of minutes-in our experiments, between 3 min to 15 min, depending on the task difficulty.
Published: 1998
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15. Bayesian Programming Formalism

Author: Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, and Juan Ahuactzin
Subjects: Algebra, Formalism (philosophy), Computer science, Bayesian programming
Published: 2013
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16. Bayesian Models Revisited

Author: Kamel Mekhnacha, Pierre Bessière, Juan Ahuactzin, and Emmanuel Mazer
Subjects: business.industry, Bayesian probability, Artificial intelligence, business, Machine learning, computer.software_genre, computer
Published: 2013
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17. Bayesian Programming

Author: Pierre Bessière, Emmanuel Mazer, Juan Manuel Ahuactzin-Larios, Kamel Mekhnacha, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Probayes [Montbonnot], Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 0202 electrical engineering, electronic engineering, information engineering, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, 02 engineering and technology, 030217 neurology & neurosurgery, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Features Presents a new modeling methodology and inference algorithms for Bayesian programming Explains how to build efficient Bayesian models Addresses controversies, historical notes, epistemological debates, and tricky technical questions in a dedicated chapter separate from the main text Encourages further research on new programming languages and specialized hardware for computing large-scale Bayesian inference problems Offers an online Python package for running and modifying the Python program examples in the book Summary Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain Data Emphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming. Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields. Formalism and Algorithms The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems. FAQs Along with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian Computer A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
Published: 2013

18. On the Robustness of Stochastic Bayesian Machines

Author: Emmanuel Mazer, Nacer-Eddine Zergainoh, Raoul Velazco, Miguel Solinas, Juan A. Fraire, Raphaël Laurent, Said Karaoui, Alexandre Siqueira Guedes Coelho, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: Nuclear and High Energy Physics, Stochastic computing, 010308 nuclear & particles physics, Computer science, Stochastic process, Distributed computing, Bayesian probability, Probabilistic logic, 02 engineering and technology, Fault injection, Bayesian inference, 01 natural sciences, 020202 computer hardware & architecture, PACS 8542, Nuclear Energy and Engineering, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Electrical and Electronic Engineering, Register-transfer level
Abstract: This paper revisits the stochastic computing paradigm as a way to implement architectures dedicated to probabilistic inference. In general, it is assumed the operation over stochastic bit streams is robust with respect to radiation transient events effects. Moreover, it can be expected that leveraging the stochastic computing paradigm to implement probabilistic computations such as Bayesian inference implemented in hardware could yield an increased resilience to radiation effects comparatively to deterministic procedures. However, the practical assessment of the robustness against radiation is mandatory before considering stochastic Bayesian machines (SBMs) in hazardous environments. Results of fault injection campaigns at register transfer level provide the first evidences of the intrinsic robustness of SBMs with respect to single event upsets and single event transients.
Published: 2017
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19. Collusion and Corruption Risk Analysis Using Naïve Bayes Classifiers

Author: Paulo Roberto Cobbe, Emmanuel Mazer, Pierre Bessière, and Remis Balaniuk
Subjects: Risk analysis, Naive Bayes classifier, Procurement, Risk analysis (engineering), Joint audit, ComputingMilieux_LEGALASPECTSOFCOMPUTING, Information technology audit, Business, Audit plan, Audit, Performance audit
Abstract: Fighting corruption connected with public procurement and governmental agencies requires a strong and effective audit function. The scale and the complexity of the roles to be considered can prevent the use of most audit methods and technologies so successful on the corporate world. The aim of this chapter is to propose a data mining method, based on naive Bayes classifiers, to support a generic risk assessment process for audit planning. The method can sort auditable units by total risk score, fostering dedicated audit coverage to high-risk areas. Audit organizations can transition from a reactive response to a proactive approach to identify and correct issues that may be indicative of fraud, waste or abuse. Extensive databases containing records from government operations can be combined to auditors knowledge, fraud profiles, impact factors or any other relevant metric in order to rank an audit universe.
Published: 2013
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20. Corruption risk analysis using semi-supervised naïve Bayes classifiers

Author: Remis Balaniuk, Pierre Bessière, Emmanuel Mazer, Paulo Roberto Cobbe, Mestrado em Gestão do Conhecimento e da Tecnologia da Informação (UCB/MGCTI), Universidade Católica de Brasília=Catholic University of Brasília (UCB), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Universidade Católica de Brasília, and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Risk analysis, Government, Actuarial science, General Computer Science, Corruption, Computer science, media_common.quotation_subject, General Engineering, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, ComputingMilieux_LEGALASPECTSOFCOMPUTING, 02 engineering and technology, Audit, 16. Peace & justice, Computer security, computer.software_genre, Naive Bayes classifier, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Risk assessment, computer, media_common
Abstract: International audience; In this paper, we consider the application of a naïve Bayes model for the evaluation of corruption risk associated with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for the detection of corruption in government transactions, and assisting audit agencies in becoming more proactive regarding corruption detection and prevention.
Published: 2013
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21. The Ariadne's Clew Algorithm

Author: Juan-Manuel Ahuactzin, Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leibniz (Leibniz - IMAG), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, Laboratoire de Génie Informatique - LAboratoire de SCiences COgnitives (IMAG-LGI / LASCO3), IMAG, Bessiere, Pierre, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Université Joseph Fourier - Grenoble 1 (UJF)-IMAG-Centre National de la Recherche Scientifique (CNRS), Departamento de Ingeniería En Sistemas Computacionales, Universidad de las Américas, and Laugier, Christian
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, 0209 industrial biotechnology, Optimization problem, Computer science, Computer Science - Artificial Intelligence, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Artificial Intelligence, [SCCO.COMP] Cognitive science/Computer science, 0202 electrical engineering, electronic engineering, information engineering, Six degrees of freedom, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Motion planning, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, Degrees of freedom, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Artificial Intelligence (cs.AI), Obstacle, Path (graph theory), Robot, 020201 artificial intelligence & image processing, Algorithm
Abstract: http://www.jair.org/papers/paper468.html; International audience; We present a new approach to path planning, called the Ariadne's clew algorithm. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.
Published: 2011
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22. Bayesian Approach to Action Selection and Attention Focusing

Author: Emmanuel Mazer, Pierre Bessière, Carla Koike, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 02 engineering and technology, Machine learning, computer.software_genre, Action selection, Motor model, 020901 industrial engineering & automation, Joint probability distribution, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Published: 2008
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23. 3D Human Hip Volume Reconstruction with Incomplete Multimodal Medical Images

Author: Francois Leitner, Christian Laugier, Emmanuel Mazer, Juan-Manuel Ahuactzin, and Miriam Amavizca
Subjects: medicine.medical_specialty, business.industry, General surgery, Orthopedic surgery, medicine, Total hip replacement, Context (language use), Volume reconstruction, business, Surgery
Abstract: This work is within the context of Computer-assisted Orthopaedic Surgery (CAOS), in particular Total Hip Replacement (THR) surgery.
Published: 2008
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24. A practical system for planning safe trajectories for manipulator robots

Author: Christian Laugier, Jocelyne Troccaz, Emmanuel Mazer, and Christine Bellier
Subjects: Iterative method, business.industry, Computer science, Parallel manipulator, Control engineering, Robotics, Robot control, Control theory, Path (graph theory), Trajectory, Robot, Six degrees of freedom, Artificial intelligence, business
Abstract: This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors ...) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, we have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system including a solid modeler dedicated to robotics applications and a connection to a robot controller.
Published: 2006
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25. Handey: A robot system that recognizes, plans, and manipulates

Author: Patrick A. O'Donnell, W.E.L. Grimson, A. Lanusse, Joseph L. Jones, Tomás Lozano-Pérez, P. Tournassoud, and Emmanuel Mazer
Subjects: Engineering, Depth map, business.industry, GRASP, Computer vision, Mobile robot, Artificial intelligence, Motion planning, Motion control, business, Robotic arm, Structured light, Robot control
Abstract: We describe a robot system capable of locating a part in an unstructured pile of objects, choose a grasp on the part, plan a motion to reach the part safely, and plan a motion to place the part at a commanded position. The system requires as input a polyhedral world model including models of the part to be manipulated, the robot arm, and any other fixed objects in the environment. In addition, the system builds a depth map, using structured light, of the area where the part is to be found initially. Any other objects present in that area do not have to be modeled.
Published: 2005
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26. Merging probabilistic models of navigation: the Bayesian Map and the Superposition operator

Author: Pierre Bessière, Julien Diard, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Superposition operator, 0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Mobile robot, 02 engineering and technology, Machine learning, computer.software_genre, Mobile robot navigation, Formalism (philosophy of mathematics), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Algorithm, computer
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2005/DBM05/; International audience; This paper deals with the probabilistic modeling of space, in the context of mobile robot navigation. We define a formalism called the Bayesian Map, which allows incremental building of models, thanks to the Superposition operator, which is a formally well-defined operator. Firstly, we present a syntactic version of this operator, and secondly, a version where the previously obtained model is enriched by experimental learning. In the resulting map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.
Published: 2005

27. Proscriptive bavesian programming application for collision avoidance

Author: Cédric Pradalier, Carla Koike, Emmanuel Mazer, and Pierre Bessière
Subjects: Engineering, business.industry, Obstacle avoidance, Probabilistic logic, Bayesian programming, Situated robotics, Robotics, Artificial intelligence, business, Programmer, Field (computer science), Collision avoidance
Abstract: Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr.
Published: 2004
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28. Hierarchies of probabilistic models of navigation: the Bayesian Map and the Abstraction operator

Author: Julien Diard, Emmanuel Mazer, Pierre Bessière, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, Hierarchy, business.industry, Computer science, Divergence-from-randomness model, Bayesian probability, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Statistical model, 02 engineering and technology, 020901 industrial engineering & automation, Probabilistic CTL, 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, Bayesian programming, Artificial intelligence, business, Probabilistic relevance model
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/DBM04/ address: New Orleans, LA (US); This paper presents a new method for probabilistic modeling of space, called the Bayesian Map formalism. It offers a generalization of some common approaches found in the literature, as it does not constrain the dependency structure of the probabilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction operator. In the resulting hierarchy, localization in the high level model is based on probabilistic competition of the lower level models. Experimental results validate the concept, and hint at its usefulness for large scale scenarios.
Published: 2004

29. A theoretical comparison of probabilistic and biomimetic models of mobile robot navigation

Author: Pierre Bessière, Julien Diard, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, Engineering, business.industry, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Inference, Mobile robot, 02 engineering and technology, Bayesian inference, Mobile robot navigation, Computer Science::Robotics, 020901 industrial engineering & automation, Probabilistic method, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Bayesian programming, Artificial intelligence, Motion planning, business
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/DBM04a/ address: New Orleans, LA (US); This paper deals with the domain of space modeling for mobile robotics. It offers a comparison of probabilistic and biomimetic models of navigation. Both approaches are shown to be quite complementary: while the probabilistic methods exploit sound theoretical grounds, they lack the modularity and, as a consequence, flexibility, of their biomimetic counterparts. We propose a new formalism, called the Bayesian Map formalism, that attempts to bridge the gap between the two domains: it is based on Bayesian modeling and inference for defining the building blocks, and uses operators for building hierarchies of models.
Published: 2004

30. Bayesian Robot Programming

Author: Pierre Bessière, Julien Diard, Olivier Lebeltel, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, Iterative and incremental development, business.industry, Computer science, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, Robotics, 02 engineering and technology, Bayesian inference, Sensor fusion, Machine learning, computer.software_genre, Robot learning, 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Functional reactive programming
Abstract: International audience; We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics.
Published: 2004
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31. Programmation bayésienne des robots

Author: Olivier Lebeltel, Pierre Bessière, Emmanuel Mazer, Julien Diard, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0209 industrial biotechnology, 020901 industrial engineering & automation, programmation des robots, Artificial Intelligence, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, robotique autonome, inférence bayésienne, Software
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/LBDM04a/; Cet article propose une mthode originale de programmation des robots fonde sur linfrence et lapprentissage baysien. Cette mthode traite formellement des problmes dincertitude et dincompltude inhrents au domaine considr. La principale difficult de la programmation des robots vient de linvitable incompltude des modles utiliss. Nous exposons le formalisme de description dune tche robotique ainsi que les mthodes de rsolution. Nous lillustrons en utilisant ce systme pour programmer une application de surveillance pour un robot mobile : le Khepera. Pour cela, nous utilisons des ressources gnriques de programmation appeles descriptions . Nous montrons comment dfinir et utiliser de manire incrmentale ces ressources (comportements ractifs, fusion capteur, reconnaissance de situations et squences de comportements) dans un cadre systmatique et unifi
Published: 2004

32. Multi-sensor data fusion using Bayesian programming: An automotive application

Author: Emmanuel Mazer, Pierre Bessière, T. Fraichard, Christophe Coué, Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), and French programme La Route Automatisée (http://www.lara.prd.fr) - European project IST-1999-12224 'Sensing of Car Environment at Low Speed Driving' (http://www.carsense.org)
Subjects: 0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, Probabilistic logic, Automotive industry, [SCCO.COMP]Cognitive science/Computer science, Advanced driver assistance systems, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Sensor fusion, Machine learning, computer.software_genre, Robot control, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Bayesian programming, Computer vision, Artificial intelligence, business, computer
Abstract: International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.
Published: 2003
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33. Combining probabilistic models of space for mobile robots: the Bayesian Map and the Superposition operator

Author: Julien Diard, Pierre Bessière, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2003/DBM03a/ note: Int. Workshop on Service, Assistive and Personal Robots. Technical Challenges and Real World Application Perspectives address: Madrid (ES) editor: Armada, M. and {Gonzalez de Santos}, Pa; This paper deals with the probabilistic modeling of an environment that a robot has to navigate in. We use a method for the probabilistic modeling of space cal led the Bayesian Map formalism. This formalism al lows incremental building of models: we define the Super- position operator, which is a formal ly wel l-defined op- erator. We present first a syntactic version of this operator, and second, a version where the previously obtained model is refined and enriched by experimen- tal learning. In the resulting superposed map, loca- tions are the conjunction of underlying possible loca- tions, which al lows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.
Published: 2003

34. Hierarchies of probabilistic models of space for mobile robots: the bayesian map and the abstraction operator

Author: Julien Diard, Pierre Bessière, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Abstract: voir basilic : http://emotion.inrialpes.fr/bibemotion/2003/DBM03b/ address: Acapulco (MX); This paper presents a new method for probabilistic modelling of space, called the Bayesian Map for- malism. It offers a generalization of some com- mon approaches found in the literature, as it does not constrain the dependency structure of the prob- abilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction Operator. In the resulting hierarchy, lo- calization in the high level model is based on prob- abilistic competition of the lower level models. Ex- perimental results validate the concept, and hint at its usefulness for large scale scenarios.
Published: 2003

35. A Survey of Probabilistic Models Using the Bayesian Programming Methodology as a Unifying Framework

Author: Julien Diard, Pierre Bessière, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Robotics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; This paper presents a survey of the most common probabilistic models for artefact conception. We use a generic formalism called Bayesian Programming, which we introduce briefly, for reviewing the main probabilistic models found in the literature. Indeed, we show that Bayesian Networks, Markov Localization, Kalman filters, etc., can all be captured under this single formalism. We believe it offers the novice reader a good introduction to these models, while still providing the experience reader an enriching global view of the field.
Published: 2003

36. Proscriptive Bayesian Programming Application for Collision Avoidance

Author: Carla Koike, Cédric Pradalier, Pierre Bessière, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), European project BIBA (IST-2001-32115), and Carla Koike is financially supported by CAPES/Brazilian Government
Subjects: [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Robotics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http:// www-laplace.imag.fr
Published: 2003

37. Tele-act: A task level teleprogramming system

Author: M.Y. Kaczor, Christian Laugier, and Emmanuel Mazer
Subjects: Engineering, Telerobotics, Supervisory control, business.industry, Controller (computing), Real-time computing, Testbed, Robot, Workcell, business, Control room, Simulation, Haptic technology
Abstract: The authors address the problem of controlling a remote arm when a large communication delay exists between the control room and the remote workcell. To minimize the volume of information transiting through the communication link and to avoid the problems due to the delay of transmission, the system makes use of two identical world models of the remote workcell: one installed physically close to the operator and the other installed within the controller of the remote site. The communication link only transmits task level robot commands or goals to be achieved. Both sites are equipped with two sets of identical planners, one set is used to test if the generated task level commands can be processed by these planners and the other to plan and execute the actual commands on the remote workcell. After an overview of existing teleprogramming environments, the authors present a general description of the system. Finally they describe the experimental testbed and the results obtained with their method.
Published: 2002
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38. START: an application builder for industrial robotics

Author: S. Geoffroy, M. Tounsi, G. Boismain, F. Verdot, J.M. Dubourdieu, Y. Douillard, Emmanuel Mazer, and J.M.B. Des Tuves
Subjects: Engineering, business.industry, Nuclear power, Application software, computer.software_genre, Control theory, Robot, Reference architecture, Architecture, Software architecture, business, Software engineering, computer, Graphical user interface
Abstract: The repair of nuclear power plants often necessitates the development of special robots which should be operational readily. This requires a software architecture which quickly permits the design of a controller for an entire telerobotic system in a minimum period of time. We have developed such a software architecture to facilitate the design of applications dealing with nonstandard robot systems and complex industrial processes. In this paper we describe the requirements for this architecture and how we address them.
Published: 2002
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39. On manipulation planning

Author: J.M. Ahuactzin, Kamal Gupta, and Emmanuel Mazer
Subjects: Computer Science::Robotics, Set (abstract data type), Minimisation (psychology), Mathematical optimization, Landmark, Robot, Motion (geometry), Configuration space, Motion planning, Algorithm, Any-angle path planning, Mathematics
Abstract: An emerging paradigm in solving the classical motion planning problem (among static obstacles) is to capture the connectivity of the configuration space using a finite (but possibly large) set of landmarks (or nodes) in it. In this paper, the authors extend this paradigm to manipulation planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial configuration to a final configuration while avoiding collisions with the static obstacles in the environment. The authors' specific approach adapts Adraine's Clew Algorithm that has been shown effective for classical motion planning problem. In the authors' approach, landmarks are placed in lower dimensional submanifolds of the composite configuration space. These landmarks represent stable grasps that are reachable from the initial configuration. From each new landmark, the planner attempts to reach the goal configuration by executing a local planner, again in a lower (but different) dimensional submanifold of the composite configuration space. The authors have implemented this approach and present initial experiments with a simple 2-DOF planar arm among polygonal obstacles.
Published: 2002
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40. OrthoPilot: Vers une approche Bayésienne de la détermination des centres articulaires

Author: Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Laugier Yann, Franck Leitner, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Gestes Medico-chirurgicaux Assistés par Ordinateur (TIMC-IMAG-GMCAO), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), and VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience
Published: 2002

41. Pour décider, mieux vaut parier

Author: Pierre Bessière, Emmanuel Mazer, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes
Subjects: [SCCO.COMP]Cognitive science/Computer science, ComputingMilieux_MISCELLANEOUS
Abstract: National audience
Published: 2002

42. Using Bayesian Programming for Multi-Sensor Data Fusion in Automotive Applications

Author: Christophe Coué, Thierry Fraichard, Pierre Bessière, Emmanuel Mazer, Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), and French programme 'La route automatisée' (http://www.lara.prd.fr) Europen project IST-1999-12224 'Sensing of Car Environment at Low Speed Driving' (http://www.carsense.org)
Subjects: [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing sytem providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.
Published: 2002

43. A Bayesian framework for geometric uncertainties handling

Author: Emmanuel Mazer, Pierre Bessière, Kamel Mekhnacha, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Mathematical optimization, Robot kinematics, Generalization, 05 social sciences, Bayesian probability, Monte Carlo method, [SCCO.COMP]Cognitive science/Computer science, CAD, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Inverse problem, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Constraint (information theory), 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 050207 economics, Mathematics
Abstract: International audience; We present a Bayesian CAD modeler for robotic applications. We describe the methodology we use to represent and handle uncertainties using probability distributions on the system parameters and sensor measurements. We address the problem of the propagation of geometric uncertainties and how to take this propagation into account when solving inverse problems. The proposed approach may be seen as a generalization of constraint-based approaches where we express a constraint as a probability distribution instead of a simple equality or inequality. We also describe appropriate numerical algorithms used to apply this methodology. Using an example, we show how to apply our approach by providing simulation results using the implemented CAD modeler.
Published: 2001
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44. A Bayesian CAD system for robotic Applications

Author: Kamel Mekhnacha, Emmanuel Mazer, Pierre Bessière, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Geometric constraints, Bayesian reasoning, [SCCO.COMP]Cognitive science/Computer science, CAD, Monte Carlo methods, Robotics, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; We present in this paper a Bayesian CAD system for robotic applications. We address the problem of the propagation of geometric uncertainties, and how to take into account this propagation when solving inverse problems. The methodology used to represent and handle uncertainties using conditional probability distributions on the system's parameters and the sensor measurements is presented. It may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using this methodology, a constraint is represented by a probability distribution instead of a simple equality or inequality. Numerical algorithms used to apply this methodology are also described. Using an example, we show how to apply our approach by providing simulation results using our CAD system.
Published: 2000

45. Interprétation versus Description (I) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs

Author: Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Kamel Mekhnacha, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Bessiere, Pierre
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], [SCCO.COMP]Cognitive science/Computer science, science cognitive, robotique, logique, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], inférence bayésienne, perception, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], intelligence artificielle, inférence probabiliste, [SCCO.COMP] Cognitive science/Computer science, décision, action, entropie maximum
Abstract: Jusqu'à quel point agir et percevoir supposent-ils de "comprendre" ou même plus simplement de se "représenter" le monde ? Telle est l'une des préoccupations fondamentales des recherches sur la sensori-motricité au centre de nombreux débats en sciences cognitives. Cette question une fois formalisée, expurgée, simplifiée et traduite en termes mathématiques, nous amène à nous interroger tout au long de cet article, sur les liens qui peuvent exister entre les inférences formelles mécanisées informatiquement et leurs contreparties dans le monde physique où évolue un robot. Ainsi reformulée, la question centrale débattue devient : comment rendre effectives les inférences formelles ?
Published: 1999

46. Interprétation ou Description (II) : Fondements mathématiques de l'approche F+D

Author: Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], [SCCO.COMP] Cognitive science/Computer science, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: Cet article fait suite et complète l'article intitulé "Interprétation ou Description : proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs". L'objectif poursuivi est de présenter les principes des fondements mathématiques de l'approche F+D. E.T. Jaynes propose dans (Jaynes95) une théorie de la cognition fondée sur les probabilités appelée "Probability as Logic" (PaL). Cette théorie fournit les deux composantes fondamentales dont nous avons besoin pour notre approche F+D, d'une part, des règles formelles permettant de raisonner sur ces données incertaines et incomplètes, d'autre part, le principe de maximum d'entropie, qui permet de clarifier le lien entre descriptions et expériences et donne un cadre théorique général pour l'apprentissage.
Published: 1998

47. Modélisation bayésienne d'interactions robot/environnement téléopérées

Author: Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published: 1997

48. Modeleur probabiliste pour la CAO robotique

Author: Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published: 1997

49. Méthode probabiliste bayésienne pour la calibration en robotique

Author: Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, Bessiere, Pierre, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), and Institut National Polytechnique de Grenoble (INPG)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published: 1996

50. Fast direct and inverse model acquisition by function decomposition

Author: R. Balaniuk, Emmanuel Mazer, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Generalized inverse, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Functional decomposition, Electronic mail, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data acquisition, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, 050205 econometrics, Mathematics, business.industry, 05 social sciences, Robotics, Function (mathematics), Inverse problem, 020201 artificial intelligence & image processing, Artificial intelligence, Multidimensional systems, business, computer, Algorithm
Abstract: A computational approach to direct and generalized inverse model acquisition is presented. The approach is based on a proposed method to direct model acquisition from partial information. The method decomposes a hyper-space function in one variable functions, simplifying the learning problem. The acquired direct model is then implemented in a tree-like structure that can be used in the inverse sense without additional learning effort. The authors' approach is able to acquire complete models in hyper-spaces requiring only selected data focused in one-dimension sub-spaces, strongly reducing the data acquisition effort. The authors' approach is particularly interesting for applications in robotics. The acquisition of direct models in robotics frequently takes place in high dimension phase spaces. When traditional approximation methods are used, enormous data bases, containing the examples to be interpolated, are required.
Published: 1995

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