Manipulation of perceptions is a remarkable human capability in a wide variety of physical and mental tasks under fuzzy or uncertain surroundings. Possibilistic reasoning can be treated as a mechanism that mimics human inference mechanisms with uncertain information. Petri nets are a graphical and mathematical modeling tool with powerful modeling and analytical ability. The focus of this paper ...

Logic programs with possibilistic ordered disjunction (or LPPODs) are a recently defined logic-programming framework based on logic programs with ordered disjunction and possibilistic logic. The framework inherits the properties of such formalisms and merging them, it supports a reasoning which is nonmonotonic, preferenceand uncertain-aware. The LPPODs syntax allows to specify 1) preferences in...

In this paper we present a propositional logic programming language for reasoning under possibilistic uncertainty and represent­ ing vague knowledge. Formulas are repre­ sented by pairs (ip, a), where ip is a many­ valued proposition and a E [0, 1] is a lower bound on the belief on ip in terms of necessity measures. Belief states are modeled by pos­ sibility distributions on the set of all many...

In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restricted in the sense that the input information can only be a formula instead of a possibilistic knowledge base which is a set of weighted formulas. To break this restriction, we cons...

Article history: Received 1 January 2010 Received in revised form 5 May 2010 Accepted 18 May 2010 Available online 18 May 2010 In this paper, we present a multi-objective possibilistic programming model to locate distribution centers (DCs) and allocate customers' demands in a supply chain network design (SCND) problem. The SCND problem deals with determining locations of facilities (DCs and/or ...

Possibilitic networks, which are compact representations of possibility distributions, are powerful tools for representing and reasoning with uncertain and incomplete knowledge. According to the operator conditioning is based on, there are two possibilistic settings: quantitative and qualitative. This paper deals with qualitative possibilistic network classifiers under uncertain inputs. More pr...

This paper discusses a class of uncertain optimization problems, in which unknown parameters are modeled by fuzzy intervals. The membership functions the intervals interpreted as possibility distributions for values parameters. It is shown how known concepts robustness and light robustness, traditional interval uncertainty representation parameters, can be generalized to choose solutions that o...

This paper describes the use of syntactical data fusion to computing possibilistic qualitative decisions. More precisely qualitative possibilistic decisions can be viewed as a data fusion problem of two particular possibility distributions (or possibilistic knowledge bases): the first one representing the beliefs of an agent and the second one representing the qualitative utility. The proposed ...

This paper addresses the problem of merg­ ing uncertain information in the framework of possibilistic logic. It presents several syn­ tactic combination rules to merge possibilis­ tic knowledge bases, provided by different sources, into a new possibilistic knowledge base. These combination rules are first de­ scribed at the meta-level outside the lan­ guage of possibilistic logic. Next, an exte...