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Benferhat S.,University of Lille Nord de France | Benferhat S.,University of Artois | Benferhat S.,CNRS Lens Computer Science Research Center
Artificial Intelligence | Year: 2010

Causality and belief change play an important role in many applications. This paper focuses on the main issues of causality and interventions in possibilistic graphical models. We show that interventions, which are very useful for representing causal relations between events, can be naturally viewed as a belief change process. In particular, interventions can be handled using a possibilistic counterpart of Jeffrey's rule of conditioning under uncertain inputs. This paper also addresses new issues that are arisen in the revision of graphical models when handling interventions. We first argue that the order in which observations and interventions are introduced is very important. Then we show that in order to correctly handle sequences of observations and interventions, one needs to change the structure of possibilistic networks. Lastly, an efficient procedure for revising possibilistic causal trees is provided. © 2009 Elsevier B.V. All rights reserved. Source

Raddaoui B.,CNRS Lens Computer Science Research Center
ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings | Year: 2015

Measuring the degree of conflict of a knowledge base can help us to deal with inconsistencies. Several semantic and syntax based approaches have been proposed separately. In this paper, we use logical argumentation as a field to compute the inconsistency measure for propositional formulae. We show using the complete argumentation tree that our family of measures is able to express finely the inconsistency of a formula following their context and allows us to distinguish between formulae. We extend our measure to quantify the degree of inconsistency of set of formulae and give a general formulation of the inconsistency using some logical properties. Source

Mengel S.,CNRS Lens Computer Science Research Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size n and modular incidence treewidth k whose smallest DNNF-encoding has size nΩ (k), and - there are CNF formulas of size n and incidence neighborhood diversity k whose smallest DNNF-encoding has size nΩ (√k). These results complement recent upper bounds for compiling CNF into DNNF and strengthen-quantitatively and qualitatively-known conditional lower bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth. © Springer International Publishing Switzerland 2016. Source

Gharbi N.,CNRS Lens Computer Science Research Center
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2016

In the latest years and with the advancements of the multicore computing world, the constraint programming community tried to benefit from the capacity of new machines and make the best use of them through several parallel schemes for constraint solving. In this paper, we present a new approach using parallel consistencies to enhance the classical solving process. Specifically, we propose an approach where a master process tries to solve a constraint satisfaction problem while using the results of consistency tests done by auxiliary workers so as to avoid some useless branching. © 2015 IEEE. Source

Gregoire E.,CNRS Lens Computer Science Research Center
Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014 | Year: 2014

We propose and experiment a practical multi-level approach to maintain contradiction-free knowledge when some incoming additional information that can contradict the preexisting knowledge must be taken into account. The approach implements an any-time strategy that triggers successive reasoning paradigms ranging from credulous to computationally more intensive forms of skepticism about conflicting information. It makes use of recent dramatic computational progress in constraint satisfaction techniques for finite domains and Boolean-related search and reasoning. © 2014 IEEE. Source

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