Uncertainty In Knowledge-Based Systems Research Articles

From Semantic Games to Provability: The Case of G\xf6del Logic

We present a semantic game for Gödel logic and its extensions, where the players’ interaction stepwise reduces arbitrary claims about the relative order of truth degrees of complex formulas to atomic ones. The paper builds on a previously developed game for Gödel logic with projection operator in Fermüller et al. (in: M.-J. Lesot, S. Vieira, M.Z. Reformat, J.P. Carvalho, A. Wilbik, B. Bouchon-Meunier, and R.R. Yager, (eds.), Information processing and management of uncertainty in knowledge-based systems, Springer, Cham, 2020, pp. 257–270). This game is extended to cover Gödel logic with involutive negations and constants, and then lifted to a provability game using the concept of disjunctive strategies. Winning strategies in the provability game, with and without constants and involutive negations, turn out to correspond to analytic proofs in a version of text{ SeqGZL } (A. Ciabattoni, and T. Vetterlein, Fuzzy Sets and Systems 161(14):1941–1958, 2010) and in a sequent-of-relations calculus (M. Baaz, and Ch.G. Fermüller, in: N.V. Murray, (ed.), Automated reasoning with analytic tableaux and related methods, Springer, Berlin, 1999, pp. 36–51) respectively.

Recent advances in genetic fuzzy systems

Abstract This special issue encompasses 11 papers devoted to the recent developments in the field of “genetic fuzzy systems”. The issue originated from presentations by nine of the authors at the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'2000) that was held in Madrid, Spain, July 3–7, 2000, seven of them given in a series of two invited sessions organized by the guest editors of this special issue. These nine original contributions have been thoroughly revised and expanded to become the papers currently presented in this issue. The two remaining contributions have been written by recognized people in the field who were invited by the guest editors. First, we briefly introduce genetic fuzzy systems as a research area that combines genetic algorithms and fuzzy systems, focusing on the current state of the art in this field. Afterwards, we give an overview on the contents of the papers in the issue.

Probabilistic satisfiability with imprecise probabilities

Treatment of imprecise probabilities within the probabilistic satisfiability approach to uncertainty in knowledge-based systems is surveyed and discussed. Both probability intervals and qualitative probabilities are considered. Analytical and numerical methods to test coherence and bound the probability of a conclusion are reviewed. They use polyhedral combinatorics and advanced methods of linear programming.

AN EXTENDED FRAMEWORK FOR EVIDENTIAL REASONING SYSTEMS

Use of the Dempster-Shafer (D-S) theory of evidence to deal with uncertainty in knowledge-based systems has been widely addressed. Several AI implementations have been undertaken based on the D-S theory of evidence or the extended theory. But the representation of uncertain relationships between evidence and hypothesis groups (heuristic knowledge) is still a major problem. This paper presents an approach to representing such knowledge, in which Yen’s probabilistic multi-set mappings have been extended to evidential mappings, and Shafer’s partition technique is used to get the mass function in a complex evidence space. Then, a new graphic method for describing the knowledge is introduced which is an extension of the graphic model by Lowrance et al. Finally, an extended framework for evidential reasoning systems is specified.

A Methodology for Uncertainty in Knowledge-Based Systems.

The aims of this study.- Interval estimation of probabilities.- Related theories.- The simplest case of a diagnostic system.- Generalizations.- Interval estimation of probabilities in diagnostic systems.- A demonstration of the use of interval estimation.

The treatment of uncertainty in knowledge-based systems using fuzzy sets and possibility theory

Knowledge representation issues related to the modelling of imprecision and uncertainty are discussed in the framework of possibility theory. Differences and relations between possibility theory, fuzzy sets, probability theory and Shafer evidence theory are presented. Then, patterns of reasoning and inference and control procedures are studied in presence of uncertainty and imprecision using a possibilistic approach. the basic ideas and the main trends are emphasized rather than the mathematical and logical foundations or the technical details of implementation which can be found in the references.

Call for papers: International conference on information processing and management of uncertainty in knowledge-based systems : June 30–July 4, 1986, Paris, France

Call for papers: International conference on information processing and management of uncertainty in knowledge-based systems : June 30–July 4, 1986, Paris, France