Possibilistic logic provides a convenient tool for dealing with inconsistency and handling uncertainty. In this paper, we propose possibilistic description logics as an extension of description logics. We give semantics and syntax of possibilistic description logics. We then define two inference services in possibilistic description logics. Since possibilistic inference suffers from the drownin...

In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian netw...

Possibilistic networks and possibilistic logic bases are important tools to deal with uncertain pieces of information. Both of them offer a compact representation of possibility distributions. This paper studies a new representation format, called hybrid possibilistic networks, which cover both standard possibilistic networks and possibilistic knowledge bases. An adaptation of propagation algor...

We present a new algorithm, called Optimist, which generates possibilistic setvalued rules from tables containing categorical attributes taking a finite number of values. An example of such a rule might be “IF HOUSEHOLDSIZE={Two OR Tree} AND OCCUPATION={Professional OR Clerical} THEN PAYMENT_METHOD={CashCheck (Max=249) OR DebitCard (Max=175)}. The algorithm is based on an original formal framew...

The modeling of complex risk situations imposes the existence of multiple ways to represent the risk and compare the risk situations between them. In probabilistic models, risk is described by random variables and risk situations are compared by stochastic dominance. In possibilistic or credibilistic models, risk is represented by fuzzy variables. This paper concerns three indicators of dominan...

In this paper, we show how a possibilistic uncertainty model can be used to represent and manipulate uncertainty about the states of a system and of its components. At the same time, we present a thorough study of the incorporation of this possibilistic uncertainty model in classical, two-state reliability theory. The possibilistic reliability of a component or system is introduced and studied....

Contextuality and nonlocality are nonclassical properties exhibited by quantum statistics whose implications profoundly impact both the foundations applications of theory. In this paper we provide some insights into logical contextuality inequality-free proofs. The former can be understood as possibility version contextuality, while latter refers to proofs that not based on violations noncontex...

We provide basic results for the development of a theory of possibilistic Markov processes. We define and study possibilistic Markov processes and possibilistic Markov chains, and derive a possibilistic analogon of the Chapman-Kolmogorov equation. We also show how possibilistic Markov processes can be constructed using one-step transition possibilities.

When the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to a series of possibilistic criteria, e.g pessimistic possibilistic qualitative utility, possibilistic likely dominance , binary possibilistic utility and possibilisti...

In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses which are able to capture incomplete information and incomplete states of a knowledge base at the same time. By considering a possibilistic logic program as a possibilistic logic th...