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39 results on '"Olivier Lebeltel"'

1. Formal and Informal Methods for Multi-Core Design Space Exploration

Author

Jean-Francois Kempf, Olivier Lebeltel, and Oded Maler

Subjects
Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain.

Published
2014
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2. Exploring the Dynamics of Mass Action Systems

Author

Oded Maler, Ádám M. Halász, Olivier Lebeltel, and Ouri Maler

Subjects
Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

We present the Populus toolkit for exploring the dynamics of mass action systems under different assumptions.

Published
2013
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10. AMT 2.0: qualitative and quantitative trace analysis with extended signal temporal logic

Author

Olivier Lebeltel, Oded Maler, Dejan Nickovic, Dogan Ulus, and Thomas Ferrère

Subjects
Computer science, Property (programming), SIGNAL (programming language), 020207 software engineering, 02 engineering and technology, Signal temporal logic, Quantitative analysis (finance), Theory of computation, 0202 electrical engineering, electronic engineering, information engineering, Trace analysis, Regular expression, Algorithm, Software, Information Systems, TRACE (psycholinguistics)
Abstract

We introduce in this paper $$\text {AMT} \; 2.0$$ , a tool for qualitative and quantitative analysis of hybrid continuous and Boolean signals that combine numerical values and discrete events. The evaluation of the signals is based on rich temporal specifications expressed in extended signal temporal logic, which integrates timed regular expressions within signal temporal logic. The tool features qualitative monitoring (property satisfaction checking), trace diagnostics for explaining and justifying property violations and specification-driven measurement of quantitative features of the signal. We demonstrate the tool functionality on several running examples and case studies, and evaluate its performance.

Published
2020
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15. Exploring the Dynamics of Mass Action Systems

Author

Ádám M. Halász, Oded Maler, Ouri Maler, and Olivier Lebeltel

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Class (set theory), Pure mathematics, Quantitative Biology::Tissues and Organs, Population, Of the form, 02 engineering and technology, lcsh:QA75.5-76.95, Transformation (music), Computational Engineering, Finance, and Science (cs.CE), Computer Science - Computers and Society, 03 medical and health sciences, 020901 industrial engineering & automation, Computers and Society (cs.CY), Chemistry (relationship), Computer Science - Computational Engineering, Finance, and Science, education, Set (psychology), Computer Science::Distributed, Parallel, and Cluster Computing, 030304 developmental biology, Physics::Computational Physics, 0303 health sciences, education.field_of_study, lcsh:Mathematics, lcsh:QA1-939, Action (philosophy), Social system, lcsh:Electronic computers. Computer science
Abstract

We present the Populus toolkit for exploring the dynamics of mass action systems under different assumptions., Comment: In Proceedings HSB 2013, arXiv:1308.5724

Published
2013
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16. Exploring Synthetic Mass Action Models

Author

Olivier Lebeltel, Ádám M. Halász, Ouri Maler, and Oded Maler

Subjects
Theoretical computer science, Particle type, Action (philosophy), Computer science, Probabilistic automaton, Sensitivity (control systems), Type (model theory), Computer Science::Databases
Abstract

In this work we propose a model that can be used to study the dynamics of mass action systems, systems consisting of a large number of individuals whose behavior is influenced by other individuals that they encounter. Our approach is rather synthetic and abstract, viewing each individual as a probabilistic automaton that can be in one of finitely many discrete states. We demonstrate the type of investigations that can be carried out on such a model using the Populus toolkit. In particular, we illustrate how sensitivity to initial spatial distribution can be observed in simulation.

Published
2015
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17. Formal and Informal Methods for Multi-Core Design Space Exploration

Author

Olivier Lebeltel, Oded Maler, and Jean-Francois Kempf

Subjects
FOS: Computer and information sciences, Multi-core processor, Computer Science - Performance, Scope (project management), Design space exploration, Computer science, lcsh:Mathematics, Distributed computing, Extension (predicate logic), lcsh:QA1-939, lcsh:QA75.5-76.95, Scheduling (computing), Domain (software engineering), Software Engineering (cs.SE), Performance (cs.PF), Computer Science - Software Engineering, Software deployment, lcsh:Electronic computers. Computer science, Formal verification
Abstract

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain., In Proceedings QAPL 2014, arXiv:1406.1567

Published
2014

18. I. Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs

Author

Emmanuel Mazeret, Kamel Mekhnacha, Olivier Lebeltel, Pierre Bessière, and Eric Dedieu

Subjects
science cognitive, intelligence artificielle, robotique, logique, inference probabiliste, inférence bayésienne, entropie maximum, perception, action, décision, Philosophy, decision, cognitive science, artificial intelligence, robotics, logic, probabilistic inference, bayesian inference, maximum entropy, General Medicine, Probabilistic inference, Humanities
Abstract

Interpretation or description (I) Proposition for a probabilistic theory of cognitive sensory-motor systems. Is it necessary to "understand" or even to "represent" the world to perceive and act ? This is one of the fundamental issue in sensorimotor research. Numerous debates in cognitive sciences turn on this subject. After simplification, formalization and translation into mathematical terms, we reformulate this question as : what is the nature of the link between the automatic inferences of a computer and their intended counterparts in the physical world where a robot is to be "embodied" ?, 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. Elle est 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 inferences 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 inferences formelles ?, Bessière Pierre, Dedieu Éric, Lebeltel Olivier, Mazeret Emmanuel, Mekhnacha Kamel. I. Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs. In: Intellectica. Revue de l'Association pour la Recherche Cognitive, n°26-27, 1998/1-2. Sciences sociales et cognition. pp. 257-311.

Published
1998
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19. SpaceEx: Scalable Verification of Hybrid Systems

Author

Rodolfo Ripado, Alexandre Donzé, Thao Dang, Antoine Girard, Colas Le Guernic, Goran Frehse, Olivier Lebeltel, Oded Maler, Rajarshi Ray, Scott Cotton, VERIMAG (VERIMAG - IMAG), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Calculs Algébriques et Systèmes Dynamiques (CASYS), Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), and Ganesh Gopalakrishnan, Shaz Qadeer

Subjects
0209 industrial biotechnology, Computer science, Computation, Variable time, 020207 software engineering, 02 engineering and technology, Support function, Polyhedron, 020901 industrial engineering & automation, Reachability, Hybrid system, Scalability, 0202 electrical engineering, electronic engineering, information engineering, Hybrid automaton, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Algorithm
Abstract

International audience; We present a scalable reachability algorithm for hybrid systems with piecewise affine, non-deterministic dynamics. It combines polyhedra and support function representations of continuous sets to compute an over-approximation of the reachable states. The algorithm improves over previous work by using variable time steps to guarantee a given local error bound. In addition, we propose an improved approximation model, which drastically improves the accuracy of the algorithm. The algorithm is implemented as part of SpaceEx, a new verification platform for hybrid systems, available at spaceex.imag.fr. Experimental results of full fixed-point computations with hybrid systems with more than 100 variables illustrate the scalability of the approach.

Published
2011
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20. Parking a car using Bayesian Programming

Author

Olivier Lebeltel, P.P. Su-Jin, and Christian Laugier

Subjects
Computer Science::Robotics, Engineering, Parallel parking problem, business.industry, Bayesian probability, Path (graph theory), Probabilistic logic, Robotics, Bayesian programming, Artificial intelligence, Motion planning, business, Domain (software engineering)
Abstract

The kinematic constraints on a car limits the movements that it can follow, and this difficulty also makes planning a path a challenging problem. More precisely, the parallel parking problem has been widely addressed in the literature, but these approaches rely on more traditional methods using control laws, motion planners or artificial intelligence. There have been recently much interest in the robotics domain on using probabilistic approaches. In this paper, we propose an original formulation and resolution of the parking problem using Bayesian Robot Programming.

Published
2004
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21. 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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22. Teaching Bayesian Behaviours to Video Game Characters

Author

Anthony Arrigoni, Pierre Bessière, Olivier Lebeltel, Ronan Le Hy, 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
Finite-state machine, Computer science, business.industry, General Mathematics, media_common.quotation_subject, Bayesian probability, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], ComputingMilieux_PERSONALCOMPUTING, 020207 software engineering, 02 engineering and technology, Computer Science Applications, Game design, Control and Systems Engineering, Video game graphics, 0202 electrical engineering, electronic engineering, information engineering, Selection (linguistics), 020201 artificial intelligence & image processing, Bayesian programming, Artificial intelligence, Imitation, business, Video game, Software, media_common
Abstract

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/LABL04/; This article explores an application of Bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a first person shooter game. We show how Bayesian programming can lead to condensed and easier formalisation of finite state machine-like behaviour selection, and lend itself to learning by imitation, in a fully transparent way for the player.

Published
2004

23. Teaching Bayesian Behaviours to Videogame Characters

Author

Ronan Le Hy, Anthony Arrigoni, Pierre Bessière, Olivier Lebeltel, 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/LABL03/ address: Las vegas, NV (US)

Published
2003

24. Learning Bayesian Behaviours for Videogame Characters

Author

Ronan Le Hy, Pierre Bessière, Olivier Lebeltel, David Bellot, 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-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
2003

25. 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

26. II. Fondements mathématiques de l'approche F+D

Author

Pierre Bessière, Emmanuel Mazeret, Kamel Mekhnacha, Olivier Lebeltel, and Eric Dedieu

Subjects
perception, cognitive science, artificial intelligence, robotics, logic, probabilistic inference, bayesian inference, maximum entropy, decision, action, Philosophy, General Medicine, Probabilistic inference, science cognitive, intelligence artificielle, robotique, logique, inférence probabiliste, inférence bayésienne, entropie maximum, décision, Humanities
Abstract

Mathematical foundations of the F+D approach. This paper is a continuation of the paper entitled "Interprétation ou Description (I) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs". Its purpose is to introduce the mathematical basis of the F+D approach presented in the first paper. The two fundamental components needed in our approach are discussed. On the one hand, the formal rules to reason about uncertain and incomplete knowledge. On the other hand, the maximum entropy principle, which clarifies the link between "descriptions" and experiments and offers a clear and sound theoretical framework for learning., Cet article fait suite à l'article "Interprétation ou Description (I) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs". Son objectif est de présenter les fondements mathématiques de l'approche F+D présentés dans le premier article. Les deux composantes fondamentales dont nous avons besoin pour notre approche F+D sont présentées. D'une part, des règles formelles permettant de raisonner sur des 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., Bessière Pierre, Dedieu Éric, Lebeltel Olivier, Mazeret Emmanuel, Mekhnacha Kamel. II. Fondements mathématiques de l'approche F+D. In: Intellectica. Revue de l'Association pour la Recherche Cognitive, n°26-27, 1998/1-2. Sciences sociales et cognition. pp. 313-336.

Published
1998

27. 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

28. Wings were not designed to let animals fly

Author

Pierre Bessière, Olivier Lebeltel, Eric Dedieu, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Lecture Notes in Computer Science, and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, business.industry, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Biological evolution, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [SCCO.COMP] Cognitive science/Computer science, Feature (computer vision), Functional change, Artificial life, Obstacle avoidance, 0202 electrical engineering, electronic engineering, information engineering, Robot, 020201 artificial intelligence & image processing, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

“Functional change in structural continuity,” i.e., the opportunistic evolution of functions together with structures, is a major feature of biological evolution. However it has seldom struck a robotician's mind as very relevant for building robots, i.e., for design. This paper proposes starting points for investigating this unusual issue.

Published
1997

29. Identité fonctionnelle et identité structurelle: illustration sur le développement d'un robot

Author

Pierre Bessière, Eric Dedieu, Olivier Lebeltel, 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

30. 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

31. Contingency as a Motor for Robot Development

Author

Pierre Bessière, Eric Dedieu, Olivier Lebeltel, 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], [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

32. La poubelle lumineuse : expérience de modélisation quantitative des interactions fonctionnelles sensori-motrices

Author

Olivier Lebeltel, Pierre Bessière, Emmanuel Mazer, Bessiere, Pierre, 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
1994

33. The 'Beam in the Bin' experiment

Author

Pierre Bessière, Eric Dedieu, Olivier Lebeltel, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

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
1994

34. La poubelle lumineuse : Acquisition probabiliste d'organisation sensori-motrice

Author

Olivier Lebeltel, Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO.COMP] Cognitive science/Computer science, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Published
1994

35. Basic Concepts of Bayesian Programming

Author

Pierre Bessière, Olivier Lebeltel, 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
Computer science, business.industry, Bayesian network, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Variable-order Bayesian network, Inductive programming, 03 medical and health sciences, 0302 clinical medicine, Joint probability distribution, 0202 electrical engineering, electronic engineering, information engineering, Programming paradigm, 020201 artificial intelligence & image processing, Bayesian programming, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

The purpose of this chapter is to introduce gently the basic concepts of Bayesian programming.

36. L'ancrage perceptif des symboles ? ... Mais au fait, a-t-on vraiment besoin des symboles ?

Author

Pierre Bessière, Olivier Lebeltel, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-IMAG

Subjects
[SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

37. The 'Beam in the Bin' experiment; an application of Probability as Logic to autonomous robotic

Author

Pierre Bessière, Eric Dedieu, Emmanuel Mazer, Olivier Lebeltel, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

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]

38. AMT 2.0: Qualitative and Quantitative Trace Analysis with Extended Signal Temporal Logic

Author

Dejan Nickovic, Thomas Ferrère, Olivier Lebeltel, Dogan Ulus, and Oded Maler

Subjects
Signal temporal logic, Quantitative analysis (finance), Property (programming), Computer science, 0202 electrical engineering, electronic engineering, information engineering, 020207 software engineering, 020201 artificial intelligence & image processing, Trace analysis, 02 engineering and technology, Regular expression, Algorithm, Signal, TRACE (psycholinguistics)
Abstract

We introduce in this paper \(\textsc {AMT~2.0}\), a tool for qualitative and quantitative analysis of hybrid continuous and Boolean signals that combine numerical values and discrete events. The evaluation of the signals is based on rich temporal specifications expressed in extended Signal Temporal Logic (xSTL), which integrates Timed Regular Expressions (TRE) within Signal Temporal Logic (STL). The tool features qualitative monitoring (property satisfaction checking), trace diagnostics for explaining and justifying property violations and specification-driven measurement of quantitative features of the signal.

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39. A Bayesian framework for robotic programming

Author

Julien Diard, Olivier Lebeltel, 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
0209 industrial biotechnology, Generic programming, Computer science, business.industry, Principle of maximum entropy, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 0102 computer and information sciences, 02 engineering and technology, Bayesian inference, Sensor fusion, 01 natural sciences, Inductive programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Robotics, 020901 industrial engineering & automation, 010201 computation theory & mathematics, Programming paradigm, Reactive programming, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Artificial intelligence, business
Abstract

We propose an original method for programming robots based on bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physicist E. T. Jaynes: “Probability as Logic” [1]. Learning and maximum entropy principle translate incompleteness into uncertainty. Bayesian inference offers a formal framework for reasoning with this uncertainty. The main contribution of this paper is the definition of a generic system of robotic programming and its experimental application. We illustrate it by programming a surveillance task with a mobile robot: the Khepera. In order to do this, we use generic programming resources called “descriptions”. We show how to define and use these resources in an incremental way (reactive behaviors, sensor fusion, situation recognition and sequences of behaviors) within a systematic and unified framework.

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