Search

The present paper describes briefly a project idea in progress about the evolvement of individuals' opinions, beliefs and perceptions on social networks (such as Facebook, Twitter, Instagram, youtube...) which is a thorny subject that has whetted nowadays the curiosity of a hulk of researchers from various disciplines. For this purpose, differently from a lot of works in the literature, we rely on logical knowledge representation tools in order to investigate the belief merging operation of Artificial Intelligence (AI). The major objective of this project is to provide efficient operator for merging heterogeneous, inconsistent and uncertain multiple sources information in the context of social networks taking into account the fact that opinion can be formed and developed through the concept of social influence with its two forms (informational social influence and normative social influence) and the concept of social trust. We intend thus through this research work presenting an adaptative version to our context of an approach [7] expressed thanks to Answer Set Programming (ASP) paradigm with stable model semantics. It is worth to say that our approach profits from the impressive volume data produced by users in social networks about a particular topic by learning from opinions, beliefs and perceptions that their freinds/neighbors share and therefore allows to use this kind of data to extract initial opinions, and to validate the proposed opinions merging process allowing even the prediction of users' behaviors.

This paper deals with the complexity of model checking for belief base revision. We extend the study initiated by Liberatore & Schaerf and introduce two new belief base revision operators stemming from consistent subbases maximal with respect to cardinality. We establish the complexity of the model checking problem for various operators within the framework of propositional logic as well as in the Horn fragment.

International audience; Recently, Creignou et al. (Theory Comput. Syst. 2017) have introduced the class 1 DelayFPT into parameterised complexity theory in order to capture the notion of efficiently solvable 2 parameterised enumeration problems. In this paper, we propose a framework for parameterised 3 ordered enumeration and will show how to obtain enumeration algorithms running with an FPT delay 4 in the context of general modification problems. We study these problems considering two different 5 orders of solutions, namely, lexicographic order and order by size. Furthermore, we present two 6 generic algorithmic strategies. The first one is based on the well-known principle of self-reducibility 7 and is used in the context of lexicographic order. The second one shows that the existence of a 8 neighbourhood structure among the solutions implies the existence of an algorithm running with 9 FPT delay which outputs all solutions ordered non-decreasingly by their size. 10

The Particle Swarm Optimization (PSO) algorithm is an innovative and promising optimization technique in evolutionary computation. The Essential Particle Swarm Optimization queen (EPSOq) is one of the recent discrete PSO versions that further simplifies the PSO principles and improves its optimization ability. Hybridization is a principle of combining two (or more) approaches in a wise way such that the resulting algorithm includes the positive features of both (or all) the algorithms. This paper proposes a new heuristic approach such that various features inspired from the Tabu Search are incorporated in the EPSOq algorithm in order to obtain another improved discrete PSO version. The implementation of this idea is identified with the acronym TEPSOq (Tabu Essential Particle Swarm Optimization queen). Experimentally, this approach is able to solve optimally large-scale strongly correlated 0---1 Multidimensional Knapsack Problem (MKP) instances. Computational results show that TEPSOq has outperforms not only the EPSOq, but also other existing PSO-based approaches and some other meta-heuristics in solving the 0---1 MKP. It was discovered also that this algorithm is able to locate solutions extremely close and even equal to the best known results available in the literature.

International audience; Belief change within the framework of fragments of propo-sitional logic is one of the main and recent challenges in the knowledge representation research area. While previous research works focused on belief revision, belief merging, and belief contraction, the problem of belief update within fragments of classical logic has not been addressed so far. In the context of revision, it has been proposed to refine existing operators so that they operate within propositional fragments, and that the result of revision remains in the fragment under consideration. This approach is not restricted to the Horn fragment but also applicable to other propositional fragments like Krom and affine fragments. We generalize this notion of refinement to any belief change operator. We then focus on a specific belief change operation, namely belief update. We investigate the behavior of the refined update operators with respect to satisfaction of the KM postulates and highlight differences between revision and update in this context.