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 introduce a setting for learning possibilistic logic theories from defaults of the form “if alpha then typically beta”. We first analyse this problem from the point of view of machine learning theory, determining the VC dimension of possibilistic stratifications as well as the complexity of the associated learning problems, after which we present a heuristic learning algorithm that can easil...

This paper proposes a framework for planning under uncertainty in which the initial state of the world and the eeects of actions are described by possibility distributions. Two notions of solution plans are introduced: acceptable plans that lead to a goal state with a certainty greater than a given threshold, and optimal plans that lead to a goal state with maximal certainty. Simple characteriz...

This section investigates graphical modeling as a powerful framework for drawing inferences under imprecision and uncertainty. We survey the semantical background and relevant properties of relational, probabilistic, and possibilistic networks and consider evidence propagation in such networks as well as methods for learning them from data. Whereas the probabilistic Bayesian networks and Markov...

This article proposes a novel approach to ranking fuzzy numbers based on WLUPM ( weighted lower and upper possibilistic means ). At first, by using this significance, we will compose WIVPM ( weighted intervalvalue possibilistic mean ), that this interval can be used as a crisp set approximation with respect to a fuzzy quantity. Therefore, with that, we define a method for ordering. This method ...

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....

In this paper, we explore several statistical methods to find solutions to the problem of query translation ambiguity. Indeed, we propose and compare a new possibilistic approach for query translation derived from a probabilistic one, by applying a classical probability-possibility transformation of probability distributions, which introduces a certain tolerance in the selection of word transla...

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.