Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

We present an overview on how to perform non-monotonic reasoning based on paraconsistent logics. In particular, we show that one can define a logic programming semantics based on the paraconsistent logic G’3 which is called G’3-stable semantics. This semantics defines a frame for performing non-monotonic reasoning in domains which are pervaded with vagueness and inconsistencies. In fact, we sho...

Argumentation frameworks have proven to be a successful approach to formalizing commonsense reasoning. Recently, some argumentation frameworks have been extended to deal with possibilistic uncertainty, notably Possibilistic Defeasible Logic Programming (P-DeLP). At the same time, modelling argument accrual has gained attention from the argumentation community. Even though some preliminary forma...

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming pr...

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...

Possibilistic Defeasible Logic Programming (P-DeLP) is an argumentation framework based on logic programming which incorporates a treatment of possibilistic uncertainty at object-language level. In P-DeLP, the closure of justified conclusions is not always consistent, which has been detected to be an anomaly in the context of so-called rationality postulates for rule-based argumentation systems...

Possibilistic Defeasible Logic Programming (P-DeLP) is an argumentation framework based on logic programming which incorporates a treatment of possibilistic uncertainty at object-language level. In P-DeLP, the closure of justified conclusions is not always consistent, which has been detected to be an anomaly in the context of so-called rationality postulates for rule-based argumentation systems...