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.

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...

The possibility for the occurrence in crystals of a phenomenon, resembling turbulence, is discussed. This phenomenon, called heterophase turbulence, is manifested by the fluctuational appearance inside a crystalline sample of disordered regions randomly distributed in space. The averaged picture for such a turbulent solid is exemplified by an exactly solvable lattice-gas model. The origin of he...

Modern science has undermined belief in countless imaginary causalities. What is the nature of the relation between mind and brain? Philosophers have debated the issue for millennia, but it is only in the last twenty years that empirical evidence has begun to uncover some of the secrets of this ancient riddle. This lecture explores the possiblity that advances in neuroscience will undermine and...