Le CRIL en bref

présentation

Le Centre de Recherche en Informatique de Lens (CRIL UMR 8188) est un laboratoire de l’Université d’Artois et du CNRS qui regroupe plus de cinquante membres : chercheurs, enseignants-chercheurs, doctorants et personnels administratifs et techniques.

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Recherches en intelligence artificielle et applications

mots clés du CRIL

Actualités (RSS)

Séminaire 

Société Altifort

Séminaire Belief Change in a Preferential Non-monotonic Framework

Tommie Meyer (CAIR, South Africa)

Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this talk we will discuss that it also makes sense to analyse belief change within a non-monotonic framework, and in particular we take under consideration a preferential non-monotonic framework. We consider belief change operators in a non-monotonic propositional setting with a view towards preserving consistency. We show that the results obtained can also be applied to the preservation of coherence - an important notion within the field of logic-based ontologies. We adopt the AGM approach to belief change and show that standard AGM can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results.

Brief bio:
Tommie Meyer is a full professor in the Department of Computer Science at UCT, the UCT-CSIR Chair in Artificial Intelligence, and Director of the Centre for Artificial Intelligence Research (CAIR) at the CSIR in South Africa. Prior to this he was a chief scientist at the CSIR, a senior researcher at NICTA in Australia, conjoint associate professor at the University of New South Wales in Australia, associate professor at the University of Pretoria, and senior lecturer at the University of South Africa. He is recognised internationally as an expert in Knowledge Representation and Reasoning. He is a member of the steering committee of the International Conference on Principles of Knowledge Representation (KR), chair of the steering committee of the Nonmonotonic Reasoning workshop series, associate editor of the journal Artificial Intelligence, and editorial board member of the Journal of Artificial Intelligence Research. He has published in all the top journals and conferences in Artificial Intelligence.

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Prix de thèse AFIA pour Eric Piette

L’Association Française pour l’Intelligence Artificielle décerne chaque année
depuis 2009 un prix de thèse pour faire connaître et reconnaître les meilleurs
travaux de recherche des jeunes chercheurs en Intelligence Artificielle.

Cette année, la thèse d’Eric Piette intitulée “Une nouvelle approche au General Game Playing dirigée par les contraintes” reçoit ce prix ex-aequo.

Ce travail, encadré par Frédéric Koriche, Sylvain Lagrue et Sébastien Tabary
a donné lieu à la conception du joueur logiciel Woodstock, actuel champion du
monde de General Game Playing.

Eric Piette présentera son travail ce vendredi 6 juillet, à 14h, lors des Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA), à la plateforme IA 2017.

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Séminaire Constraint Games

Arnaud Lallouet

Constraint Games are a recent framework proposed to model and solve static games where Constraint Programming is used to express players preferences. In a recent work, we rethink the solving technique in terms of constraint propagation by considering players preferences as global constraints. It yields not only a more elegant but also a more efficient framework. Our new complete solver is faster than previous state-of-the-art and is able to find all pure Nash equilibria for some problems with 200 players. We also show that performances can greatly be improved for graphical games, allowing some games with 2000 players to be solved.

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Séminaire Developing a parallel CP (or SAT) solver through the exploitation of strong propagation methods.

Kostas Stergiou (University of Western Macedonia)

As a result of the considerable recent advances in parallel constraint solving, a number of quite efficient parallel CP and SAT solvers have been developed. Parallel constraint solving techniques are roughly divided in search space splitting and portfolio-based ones. In this talk we describe a novel scheme for developing a parallel CP (or SAT) solver through the exploitation of strong propagation methods. This scheme is orthogonal to the two general categories of parallelization methods and it is presented as a search algorithm consisting of a main process, which is a typical CP (or SAT) solver, aided by a number of coworkers running in parallel.

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