This paper proposes a new Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between terms, documents and query by possibilistic networks. The user’s query triggers a propagation process to retrieve necessarily or at least possibly relevant documents.

Many physical theories accurately predict which events are possible and which are not, or – in situations where probabilistic (e.g., quantum) effects are important – predict the probabilities of different possible outcomes. At first glance, it may seem that this probabilistic information is all we need. We show, however, that to adequately describe physicists’ reasoning, it is important to also...

The motivation of possibility theory is to represent the imprecision intrinsic in natural language, in particular in expressions of the form x is R. A suitable topos of presheaves, i.e. an extension of classical set theory, is presented where sets of the form {x : x is R} can be constructed with x is R having the meaning given in possibility theory. The aim of the paper is to give an explicit m...

The problem of expert opinions representation and aggregation has long been adressed in the only framework of probability theory. Nevertheless, recent years have witnessed many proposals in other uncertainty theories (possibility theory, evidence theory, imprecise probabilities). This paper casts the problem of aggregating expert opinions in a common underlying framework and shows how uncertain...

The problem of assessing the value of a can­ didate is viewed here as a multiple combi­ nation problem. On the one hand a candi­ date can be evaluated according to different criteria, and on the other hand several ex­ perts are supposed to assess the value of can­ didates according to each criterion. Criteria are not equally important, experts are not equally competent or reliable. Moreover lev...

The paper discusses integration in possibility theory, both in an ordinal and in a numerical (behavioral) context. It is shown that in an ordinal context, the fuzzy integral has an important part in at least three areas: the extension of possibility measures to larger domains, the construction of product measures from marginals and the definition of conditional possibilities. In a numerical (be...

Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory framework. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic...

There has been an ever-increasing interest in multi-disciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the first ...

Uncertainty importance measures typically reflect the degree to which uncertainty about risk and reliability parameters at the component level influences uncertainty about parameters at the system level. The definition of these measures is typically founded on a Bayesian perspective where subjective probabilities are used to express epistemic uncertainty; hence, they do not reflect the effect o...

When there is uncertainty about the values of a variable, it is possible to have different pieces of information about it. This can be due to the fact that they represent the points of view of different agents and these agents have followed different criteria when codify the pieces of information by means of numbers. The aim of this paper is to provide tools for the comparison of these represen...