Catálogo de publicaciones - libros

Information often comes from different sources and merging these sources usually leads to apparition of inconsistencies. Fusion is the operation which consists in restoring the consistency of the merged information by changing a minimum of the initial information. There are many fields or applications where the information can be represented by simple linear constraints. For instance in scheduling problems, some geographic information can be also expressed by linear constraints. In this paper, we are interested in linear constraints fusion in the framework of simple temporal problems (STPs). We propose a fusion method and we experiment with it on random temporal problem instances.

propositional belief merging operators are constructed from distances between the interpretations, or states, of the logic under consideration. In this paper we extend the notion of a distance between interpretations to generalised versions of propositional interpretations referred to as -. -states allow for the definition of -merging operators, which are generalisations of classical model-based merging operators. We show how -merging, combined with appropriate measures of satisfaction, can be used to construct a logical framework for : a process of intervening between parties with conflicting demands to facilitate a compromise.

A negotiation model consists of two functions: a negotiation function and a weakening function. A negotiation function is defined to choose the weakest sources and these sources will weaken their point of view using a weakening function. However, the currently available belief negotiation models are based on classical logic, which make it difficult to define weakening functions. In this paper, we define a prioritized belief negotiation model in the framework of possibilistic logic. The priority between formulae provides us with important information to decide which beliefs should be discarded. The problem of merging uncertain information from different sources is then solved by two steps. First, beliefs in the original knowledge bases will be weakened to resolve inconsistencies among them. This step is based on a prioritized belief negotiation model. Second, the knowledge bases obtained by the first step are combined using a operator or a operator in possbilistic logic.

Two conciliation processes for intelligent agents based on an iterated merge-then-revise change function for belief profiles are introduced and studied. The first approach is skeptical in the sense that at any revision step, each agent considers that her current beliefs are more important than the current beliefs of the group, while the other case is considered in the second, credulous approach. Some key features of such conciliation processes are pointed out for several merging operators; especially, the convergence issue, the existence of consensus and the properties of the induced iterated merging operators are investigated.

An important problem in the management of knowledge-based systems is the handling of inconsistency. Inconsistency may appear because the knowledge may come from different sources of information. To solve this problem, two kinds of approaches have been proposed. The first category the different bases into a unique base, and the second category of approaches, such as argumentation, accepts inconsistency and copes with it.

Recently, a “powerful” approach [7,8,13] has been proposed to merge propositional bases encoded in possibilistic logic. This approach consists of combining prioritized knowledge bases into a new prioritized knowledge base, and then to infer from this.

In this paper, we present a argumentation framework for handling inconsistency arising from the presence of multiple sources of information. Then, we will show that this framework retrieves the results of the merging operator defined in [7,8,13]. Moreover, we will show that an argumentation-based approach palliates the limits, due to the problem, of the merging operator.

Alternative approaches to the widely known pignistic transformation of belief functions are presented and analyzed. A series of various probabilistic transformations is examined namely from the point of view of their consistency with rules for belief function combination and their consistency with probabilistic upper and lower bounds.A new definition of general probabilistic transformation is introduced and a discussion of their applicability is included.

The Transferable Belief Model is a general framework for managing imprecise and uncertain information using belief functions. In this framework, the operation allows to combine information provided by a source (in the form of a belief function) with metaknowledge regarding the reliability of that source, to compute a “weakened”, less informative belief function. In this article, an extension of the discounting operation is proposed, allowing to make use of more detailed information regarding the reliability of the source in different contexts, a context being defined as a subset of the frame of discernment. Some properties of this operation are studied, and its relationship with classical discounted is explained.

In this paper, Ginsberg’s/Fitting’s theory of bilattices is invoked as a natural accommodation and powerful generalization to both intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IVFSs), serving on one hand to clarify the exact nature of the relationship between these two common extensions of fuzzy sets, and on the other hand providing a general and intuitively attractive framework for the representation of uncertain and potentially conflicting information.

It is given a new algorithm to compute a lower T-transitive approximation of a fuzzy relation that preserves symmetry. Given a reflexive and symmetric fuzzy relation, the new algorithm computes a T-indistinguishability that is contained in the fuzzy relation. It has been developed a C++ program that generates random symmetric fuzzy relations or random symmetric and reflexive fuzzy relations and computes their T-transitive closure and the new low T-transitive approximation. Average distances of the fuzzy relation with the T-transitive closure are similar than the average distances with the low T-transitive approximation.

There are fast algorithms to compute the transitive closure of a fuzzy relation, but there are only a few different algorithms that compute transitive openings from a given fuzzy relation. In this paper a method to compute a transitive opening of a reflexive and symmetric fuzzy relation is given. Even though there is not a unique transitive opening of a fuzzy relation, it is proved that the computed transitive opening closure is maximal.