The paper deals with base revision for Answer Set Programming (ASP). Base revision in classical logic is done by the removal of formulas. Exploiting the non-monotonicity of ASP allows one to propose other revision strategies, namely addition strategy or removal and/or addition strategy. These strategies allow one to define families of rule-based revision operators. The paper presents a semantic characterization of these families of revision operators in terms of answer sets. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.

Belief base revision has been studied within the answer set programming framework. We go a step further by introducing uncertainty and studying belief base revision when beliefs are represented by possibilistic logic programs under possibilistic answer set semantics and revised by certain input. The paper proposes two approaches of rule-based revision operators and presents their semantic characterization in terms of possibilistic distribution. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.

Abstract The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first-order logic program with default negation. In a preliminary step, this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the projectASPeRiX. is presented as a first-order forward chaining approach for Answer Set Computing. This project was among the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first-order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solverASPeRiX. are detailed and some experiments and comparisons with existing systems are provided.

Cognitive maps are a knowledge representation model that describes as a graph influences between concepts. Each influence is quantified by a value. The values are generally not formally defined. In this paper, we introduce a new cognitive map model, the probabilistic cognitive maps. In such maps, the values of the influences are interpreted as probability values. We define formally the semantics of this model. We also provide an operation to compute the global influence of a concept on any other one, called the probabilistic propagated influence. To show that our model is valid, we propose a procedure to represent a probabilistic cognitive map as a Bayesian network.

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Cognitive maps are a knowledge representation model that describes influences between concepts. Their building is usually done by many people. This is a difficult task for them since they have to agree on every aspect of the map. This article proposes a new method to allow these people to be the designers of their own cognitive maps. A process, called synthesis, builds then a single cognitive map from this set of maps. The divergences in the maps due to the different points of view have to be solved. To do so, preferences on the designers are defined, they are used to favor the knowledge brought by some designer over the other ones.

The paper investigates a local approach for reasoning with conditional knowledge bases (with default rules of the form "generally, if /spl alpha/ then /spl beta/" and having possibly so,ne exceptions). The idea is that when a conflict appears (due to observing exceptional situations), one first localizes the sets of pieces of information which are responsible for conflicts. Next, using a specificity principle (subclasses must be preferred to general classes), the authors attach priorities to default rules inside each conflict. These priorities, implicitly computed from the knowledge base, reflect the hierarchical structure of the knowledge base. Lastly, they rank-order and solve conflicts in a way that only minimal sets of rules are given up from the knowledge base in order to restore its consistency. This local method of dealing with conflicts addresses correctly the well known problems of specificity, irrelevance, blocking of inheritance, etc.