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

Symbolic and Quantitative Approaches to Reasoning and Uncertainty: European Conference ECSQARU '93, Granada, Spain, November 8-10, 1993. Proceedings: Lecture Notes in Computer Science, cartea 747

Editat de Michael Clarke, Rudolf Kruse, Serafin Moral
en Limba Engleză Paperback – 20 oct 1993
In recent years it has become apparent that an importantpart of the theory of artificial intelligence is concernedwith reasoning on the basis of uncertain, incomplete, orinconsistent information. A variety of formalisms have beendeveloped, including nonmonotonic logic, fuzzy sets,possibility theory, belief functions, and dynamic models ofreasoning such as belief revision and Bayesian networks.Several European research projects have been formed in thearea and the first European conference was held in 1991.This volume contains the papers accepted for presentation atECSQARU-93, the European Conference on SymbolicandQuantitative Approaches to Reasoning and Uncertainty, heldat the University of Granada, Spain, November 8-10, 1993.
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Specificații

ISBN-13: 9783540573951
ISBN-10: 354057395X
Pagini: 408
Ilustrații: XII, 396 p.
Dimensiuni: 155 x 233 x 21 mm
Greutate: 0.58 kg
Ediția:1993
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

$$\mathcal{R}\mathcal{E}\mathcal{S}$$ : 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.- Weakextensions 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.