Two characterizations of a minimum-information principle for possibilistic reasoning

Information Retrieval by Possibilistic Reasoning

The Minimum Information Principle for Discriminative Learning

Exponential models of distributions are widely used in machine learning for classification and modelling. It is well known that they can be interpreted as maximum entropy models under empirical expectation constraints. In this work, we argue that for classification tasks, mutual information is a more suitable information theoretic measure to be optimized. We show how the principle of minimum mu...

Reasoning with partially ordered information in a possibilistic logic framework

In many applications, the reliability relation associated with available information is only partially defined, while most of existing uncertainty frameworks deal with totally ordered pieces of knowledge. Partial pre-orders offer more flexibility than total pre-orders to represent incomplete knowledge. Possibilistic logic, which is an extension of classical logic, deals with totally ordered inf...

Reasoning with multiple-source information in a possibilistic logic framework

This paper addresses the problem of merging uncertain information in the framework of possibilistic logic. It presents several syntactic combination rules to merge possibilistic knowledge bases, provided by different sources, into a new possibilistic knowledge base. These combination rules are first described at the meta-level outside the language of possibilistic logic. Next, an extension of p...

Pontryagin's Minimum Principle for Fuzzy Optimal Control Problems

The objective of this article is to derive the necessary optimality conditions, known as Pontryagin's minimum principle, for fuzzy optimal control problems based on the concepts of differentiability and integrability of a fuzzy mapping that may be parameterized by the left and right-hand functions of its $alpha$-level sets.