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8 results on '"European Coordinating Committee for Artificial Intelligence"'

1. ECAI 2014 : 21st European Conference on Artificial Intelligence

Author

Schaub, Torsten, European Coordinating Committee for Artificial Intelligence, České vysoké učení technické v Praze, Friedrich, Gerhard, O'Sullivan, B., IOS Press, Schaub, Torsten, European Coordinating Committee for Artificial Intelligence, České vysoké učení technické v Praze, Friedrich, Gerhard, O'Sullivan, B., and IOS Press

Subjects
Artificial intelligence--Congresses
Abstract

The role of artificial intelligence (AI) applications in fields as diverse as medicine, economics, linguistics, logical analysis and industry continues to grow in scope and importance. AI has become integral to the effective functioning of much of the technical infrastructure we all now take for granted as part of our daily lives. This book presents the papers from the 21st biennial European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. Included here are the 158 long papers and 94 short papers selected for presentation at the conference. Many of the papers cover the fields of knowledge representation, reasoning and logic as well as agent-based and multi-agent systems, machine learning, and data mining. The proceedings of PAIS 2014 and the PAIS System Demonstrations are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.

Published
2014

2. ECAI 2010 : 19th European Conference on Artificial Intelligence: Including Prestigious Applications of Artificial Intelligence (PAIS-2010): Proceedings

Author

Studer, Rudi, Wooldridge, Michael, Coelho, Helder, Faculdade de Ciências de Lisboa, European Coordinating Committee for Artificial Intelligence, Studer, Rudi, Wooldridge, Michael, Coelho, Helder, Faculdade de Ciências de Lisboa, and European Coordinating Committee for Artificial Intelligence

Subjects
Cognition--Congresses, Knowledge, Theory of--Congresses, Robotics--Congresses, Artificial intelligence--Congresses, Expert systems (Computer science)--Congresses
Published
2010

3. ECAI 2008 : 18th European Conference on Artificial Intelligence, July 21-25, 2008, Patras, Greece: Including Prestigious Applications of Intelligent Systems (PAIS 2008): Proceedings

Author

Ghallab, Malik, European Coordinating Committee for Artificial Intelligence, Ghallab, Malik, and European Coordinating Committee for Artificial Intelligence

Subjects
Artificial intelligence--Congresses
Abstract

Includes subconference'Prestigious Applications of Intelligent Systems (PAIS 2008)'.

Published
2008

4. ECAI 2012 - 20th European Conference on Artificial Intelligence, Montpellier, France, 27/08/2012 - 31/08/2012

Author

De Raedt, Luc, Bessière, Christian, Dubois, Didier, Doherty, Patrick, Frasconi, Paolo, Heintz, Fredrik, Lucas, Peter J. F., University of Freiburg [Freiburg], Agents, Apprentissage, Contraintes (COCONUT), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Queen's University [Belfast] (QUB), Linköpings universitet, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Université de Florence, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), University of Twente [Netherlands], European Coordinating Committee for Artificial Intelligence (ECCAI), De Raedt, Luc, Bessière, Christian, Dubois, Didier, Doherty, Patrick, Frasconi, Paolo, Heintz, Fredrik, Lucas, Peter, and Grélaud, Françoise

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

ISBN: 978-1-61499-098-7 (online) ; ISSN: 0922-6389; International audience; Artificial intelligence (AI) plays a vital part in the continued development of computer science and informatics. The AI applications employed in fields such as medicine, economics, linguistics, philosophy, psychology and logical analysis, not forgetting industry, are now indispensable for the effective functioning of a multitude of systems. This book presents the papers from the 20th biennial European Conference on Artificial Intelligence, ECAI 2012, held in Montpellier, France, in August 2012. The ECAI conference remains Europe's principal opportunity for researchers and practitioners of Artificial Intelligence to gather and to discuss the latest trends and challenges in all subfields of AI, as well as to demonstrate innovative applications and uses of advanced AI technology. ECAI 2012 featured four keynote speakers, an extensive workshop program, seven invited tutorials and the new Frontiers of Artificial Intelligence track, in which six invited speakers delivered perspective talks on particularly interesting new research results, directions and trends in Artificial Intelligence or in one of its related fields. The proceedings of PAIS 2012 and the System Demonstrations Track are also included in this volume, which will be of interest to all those wishing to keep abreast of the latest developments in the field of AI.

Published
2012

5. Version Space Learning for Possibilistic Hypotheses

Author

Prade, Henri, Serrurier, Mathieu, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, European Coordinating Committee for Artificial Intelligence (ECCAI), Italian Association of Artificial Intelligence, Gerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso, and Grélaud, Françoise

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; In this paper, we are interested in learning stratified hypotheses from examples and counter-examples associated with weights that express their prototypical importance. It leads to an extension of the well-known version space learning framework. In order to do that, we emphasize that the treatment of positive and negative examples in version space learning is reminding of a bipolar revision process recently studied in the setting of possibilistic information representation. Bipolarity appears when the positive and negative sides of information are specified in a distinct way. Then, we use the possibilistic bipolar representation setting, which distinguishes between what is guaranteed to be possible, and what is simply not impossible, as a basis for extending version space learning to examples associated with possibility degrees. It allows us to define a formal framework for learning layered hypotheses.

Published
2006

6. Background default knowledge and causality ascriptions

Author

Jean-François Bonnefon, Da Silva Neves, R., Dubois, D., Prade, H., Institut Jean-Nicod (IJN), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)-Département de Philosophie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), DynamiquesSociales et Vie Politique (DSVP), Université Toulouse - Jean Jaurès (UT2J), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), European Coordinating Committee for Artificial Intelligence (ECCAI), Italian Association of Artificial Intelligence, Gerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso, and Grélaud, Françoise

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; A model is defined that predicts an agent’s ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background knowledge about the normal course of the world. Background knowledge is represented by nonmonotonic consequence relations. This enables the model to handle situations of poor information, where background knowledge is not accurate enough to be represented in, e.g., structural equations. Tentative properties of causality ascriptions are explored, i.e., preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction. Empirical data are reported to support the psychological plausibility of our basic definitions.

Published
2006

7. Compiling Possibilistic Knowledge Bases

Author

Benferhat, Salem, Prade, Henri, Centre de Recherche en Informatique de Lens (CRIL), Université d'Artois (UA)-Centre National de la Recherche Scientifique (CNRS), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, European Coordinating Committee for Artificial Intelligence (EURECAI), Gerhard Brewka, Silvia Coradeschi, Anna Perini, and Paolo Traverso

Subjects
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Possibilistic knowledge bases gather propositional formulas associated with degrees belonging to a linearly ordered scale. These degrees reflect certainty or priority, depending if the formulas encode pieces of beliefs or goals to be pursued. Possibilistic logic provides a simple format that turns to be useful for handling qualitative uncertainty, exceptions or preferences. The main result of the paper provides a way for compiling a possibilistic knowledge base in order to be able to process inference from it in polynomial time. The procedure is based on a symbolic treatment of the degrees under the form of sorted literals and on the idea of forgetting variables. The number of sorted literals that are added corresponds exactly to the number of priority levels existing in the base, and the number of binary clauses added in the compilation is also equal to this number of levels. The resulting extra compilation cost is very low.

Published
2006

8. Online diagnosis of engine Dyno test benches: a possibilistic approach

Author

Boverie, Serge, Dubois, Didier, Guérandel, Xavier, De Mouzon, Olivier, Prade, Henri, Grélaud, Françoise, Frank van Harmelen, Continental Automotive France [Toulouse], Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Institut National des Sciences Appliquées (INSA), European Coordinating Committee on Artificial Intelligence (ECCAI), and Association Francaise pour l'Intelligence Artificielle (AFAI)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Hardware_PERFORMANCEANDRELIABILITY, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; A causal diagnosis solution relying on experts' knowledge concentrates on the different malfunctions that may disturb the data acquisition process and tells the engine test bench tuning engineer which malfunction has occurred or which malfunctions are most likely suspected. The use of fuzzy sets and possibility theory provides better feedback and knowledge representation. The general architecture of the system is described, and an application of the fault-diagnosis part of this system is presented. It concerns the implementation of an (off-line) automatic knowledge formalization system and the implementation of the (on-line) possibilistic causal diagnosis process.

Published
2002

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