Symbolic and Quantitative Approaches to Reasoning and Uncertainty: European Conference Ecsqaru '93, Granada, Spain, November 8-10, 1993. Proceedings

ISBN-13: 9783540573951 / Angielski / Miękka / 1993 / 396 str.

Michael Clarke; Rudolf Kruse; Serafin Moral

Opis

In recent years it has become apparent that an important part of the theory of artificial intelligence is concerned with reasoning on the basis of uncertain, incomplete, or inconsistent information. A variety of formalisms have been developed, including nonmonotonic logic, fuzzy sets, possibility theory, belief functions, and dynamic models of reasoning such as belief revision and Bayesian networks.

Spis treści

$$\mathcal\mathcal\mathcal$$ : A formalism for reasoning with relative-strength defaults.- A semantics for open normal defaults via a modified preferential approach.- Possibilistic logic: From nonmonotonicity to logic programming.- Learning membership functions.- The use of possibilistic logic PL1 in a customizable tool for the generation of production-rule based systems.- Probabilistic network construction using the minimum description length principle.- IDAGs: A perfect map for any distribution.- Learning non probabilistic belief networks.- A practical system for defeasible reasoning and belief revision.- Influence of granularity level in fuzzy functional dependencies.- A logic for reasoning about safety in decision support systems.- Acceptability of arguments as ‘logical uncertainty’.- A temporal model theory for default logic.- Uncertainty in constraint satisfaction problems: A probabilistic approach.- Interference logic = conditional logic + frame axiom.- A unifying logical framework for reason maintenance.- Taxonomic linear theories.- Making inconsistency respectable: Part 2 — Meta-level handling of inconsistency.- Restricted access logics for inconsistent information.- Translating inaccessible worlds logic into bimodal logic.- A new approach to semantic aspects of possibilistic reasoning.- Probabilistic consistency of knowledge bases in inference systems.- Weighting independent bodies of evidence.- Default logic: Orderings and extensions.- Learning causal polytrees.- Symbolic evidence, arguments, supports and valuation networks.- A dynamic ordering relation for revision.- On extensions of marginals for decision-making.- On the semantics of negations in logic programming.- Structure learning approaches in causal probabilistics networks.- Weak extensions for default theories.- Recovering incidence functions.- On the relations between incidence calculus and ATMS.- A resolution method for a non monotonic multimodal logic.- A default logic based on epistemic states.- A Formal language for convex sets of probabilities.- A lattice-theoretic analysis of ATMS problem solving.- Examples of causal probabilistic expert systems.- A mixed approach of revision in propositional calculus.- Integrating uncertainty handling formalisms in distributed artificial intelligence.- Variations of constrained default logic.- Information sets in decision theory.- The preferential semantics of a multi-modal nonmonotonic logic.- Probability of deductibility and belief functions.- Formal properties of conditional independence in different calculi of AI.- A proof theory for Constructive Default Logic.- Plausible inference for default conditionals.- Decision-making with Belief Functions and pignistic probabilities.- Default logic and Dempster-Shafer theory.- Belief revision by expansion.

O autorze

Prof. Dr. Rudolf Kruse ist Leiter des Lehrstuhls für Neuro-Fuzzy-Systeme an der Universität Magdeburg.