Safa Yahi

Cooperative bees swarm for solving the maximum weighted satisfiability problem

Solving a NP-Complete problem precisely is spiny: the combinative explosion is the ransom of this accurateness. It is the reason for which we have often resort to approached methods assuring the obtaining of a good solution in a reasonable time. In this paper we aim to introduce a new intelligent approach or meta-heuristic named “Bees Swarm Optimization”, BSO for short, which is inspired from the behaviour of real bees. An adaptation to the features of the MAX-W-SAT problem is done to contribute to its resolution. We provide an overview of the results of empirical tests performed on the hard Johnson benchmark. A comparative study with well known procedures for MAX-W-SAT is done and shows that BSO outperforms the other evolutionary algorithms especially AC-SAT, an ant colony algorithm for SAT.

Conflicts Handling in Cooperative Intrusion Detection: A Description Logic Approach

In cooperative intrusion detection, several intrusion detection systems (IDS), network analyzers, vulnerability analyzers and other analyzers are deployed in order to get an overview of the system under consideration. In this case, the definition of a shared vocabulary describing the different information is prominent. Since these pieces of information are structured, we first propose to use description logics which ensure the reasoning decidability. Besides, the analyzers used in cooperative intrusion detection are not totally reliable. The second contribution of this paper is to handle these inconsistencies induced by the use of several analyzers using the so-called partial lexicographic inference.

Complexity and Cautiousness Results for Reasoning from Partially Preordered Belief Bases

Partially preordered belief bases are very convenient for an efficient representation of incomplete knowledge. They offer flexibility and avoid to compare unrelated pieces of information. A number of inference relations for reasoning from partially preordered belief bases have been proposed. This paper sheds light on the following approaches: the partial binary lexicographic inference, the compatible-based lexicographic inference, the democratic inference, the compatible-based inclusion inference, the strong possibilistic inference and the weak possibilistic inference. In particular, we propose to analyse these inference relations according to two key dimensions: the computational complexity and the cautiousness. It turns out that almost all the corresponding decision problems are located at most at the second level of the polynomial hierarchy. As for the cautiousness results, they genereally extend those obtained in the particular case of totally preordered belief bases.

A New Default Theories Compilation for MSP-Entailment

Handling exceptions represents one of the most important problems in Artificial Intelligence. Several approaches have been proposed for reasoning on default theories. This paper focuses on a possibilistic approach, and more precisely on the MSP-entailment (where MSP stands for Minimum Specificity Principle) from default theories which is equivalent to System P augmented by rational monotony. In order to make this entailment tractable from a computational point of view, we propose here a compilation of default theories with respect to a target compilation language. This allows us to provide polynomial algorithms to derive efficiently the MSP-conclusions of a compiled default theory. Moreover, the proposed compilation is qualified to be flexible since it efficiently takes advantage of any classical compiler and generally provides a low recompilation cost when updating a compiled default theory.

Compiling the Lexicographic Inference Using Boolean Cardinality Constraints

This paper sheds light on the lexicographic inference from stratified belief bases which is known to have desirable properties from theoretical, practical and psychological points of view. However, this inference is expensive from the computational complexity side. Indeed, it amounts to a

From using description logics to handling inconsistency in cooperative intrusion detection

\Delta_2^p

A lexicographic inference for partially preordered belief bases

-complete problem. In order to tackle this hardness, we propose in this work a new compilation of the lexicographic inference using the so-called Boolean cardinality constraints. This compilation enables a polynomial time lexicographic inference and offers the possibility to update the priority relation between the strata without any re-compilation. Moreover, it can be efficiently extended to deal with the lexicographical closure inference which takes an important place in default reasoning. Furthermore, unlike the existing compilation approaches of the lexicographic inference, ours can be efficiently parametrized by any target compilation language. In particular, it enables to take advantage of the well-known prime implicates language which has been quite influential in artificial intelligence and computer science in general.

On the compilation of stratified belief bases under linear and possibilistic logic policies

Cooperative intrusion detection consists in using several IDS and other analyzers in order to supply an overview of the system under consideration. In this case, the definition of a shared vocabulary describing the different information is prominent. Since these pieces of information are structured, we propose in this paper to use description logics which ensure the reasoning decidability. Besides, the analyzers used in cooperative intrusion detection are not totally reliable. Consequently, the cooperation could easily generate conflicts or inconsistencies. We propose in this paper to handle these inconsistencies using the so-called partial lexicographic inference.