In comparison to possibility theory, the Transferable Belief Model (TBM) – a nonproba-bilistic interpretation of Dempster-Shafer theory – enjoys little choice in terms of aggre-gation operators for the fusion of information. In this thesis, the problem of introducingsome flexibility for the combination of belief functions – the mathematical tool allowingthe representation of...

1. Preliminaries In general, for languages which allow a flexible order of major constituents, the grammatical variations of simple sentences are truth-conditionally equivalent regardless of the constituent sequences and of the intonational patterns. In Spanish, for instance, the six logically possible variations of sentences containing three constituents, namely, subject, verb, and object are ...

In this paper, we investigate the extraction of fuzzy rules from data tables based on possibility theory. A possibilistic decision language is used to represent the extracted fuzzy rules. The algorithm for rule extraction is presented and the complexity analysis is carried out. Since the results of the rule induction process strongly depend on the representation language, we also discuss some a...

Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decisi...

DL-Lite is a powerful and tractable family of description logics specifically tailored for applications that use huge volumes of data. In many real world applications, data are often provided by several and potentially conflicting sources of information having different levels of priority. Possibility theory offers a very natural framework to deal with ordinal and qualitative uncertain beliefs ...

Formal implemented models of analogy face two opposing challenges. On the one hand, they must be powerful and flexible enough to handle gradient and probabilistic data. This requires an ability to notice statistical regularities at many different levels of generality, and in many cases, to adjudicate between multiple conflicting patterns by assessing the relative strength of each, and to genera...

Valuation-based systems (VBS) can be considered as a generic uncertainty framework that has many uncertainty calculi, such as probability theory, a version of possibility theory where combination is the product t-norm, Spohn’s epistemic belief theory, and DempsterShafer belief function theory, as special cases. In this paper, we focus our attention on conditioning, which is defined using the co...

This paper motivates the study of counterpossibles (logically impossible counterfactuals) as necessary for developing a decision theory suitable for generally intelligent agents embedded within their environments. We discuss two attempts to formalize a decision theory using counterpossibles, one based on graphical models and another based on proof search.

This paper presents a version of the Situation Calculus with improved capabilities in formalizing and reasoning about nondeterministic actions. Instead of describing the eeects of such actions by posting constraints on their possible outcomes , as is typically done in the common Situation Calculus, we provide means of describing actions by the set of their possible total eeects. An important ad...

In the last dozen years or so, our understanding of modality has been much improved by means of possible-world semantics: the project of analyzing modal language by systematically specifying the conditions under which a modal sentence is true at a possible world. I hope to do the same for counterfactual conditionals. I write A [5]-~ C for the counter-factual conditional with antecedent A and co...