Foundations of Knowledge Representation and Reasoning / Edition 1

by Gerhard Lakemeyer, Bernhard Nebel

Overview

This collection of thoroughly refereed papers presents state-of-the-art research results by well-known researchers on the foundations of knowledge representation and reasoning. In addition, there are two surveys, one by the volume editors intended as a guide to this book and another by Shoham and Cousins on mental attitudes.

In total, the volume provides a well-organized report on current research in knowledge representation, which is one of the central subfields of AI. Except the surveys, the papers grew out of a workshop on Theoretical Foundations of Knowledge Representation and Reasoning, held in conjunction with the 10th European Conference on Artificial Intelligence (ECAI-92) in Vienna in August 1992.

Product Details

ISBN-13:	9783540581079
Publisher:	Springer Berlin Heidelberg
Publication date:	07/28/1994
Series:	Lecture Notes in Computer Science , #810
Edition description:	1994
Pages:	363
Product dimensions:	6.10(w) x 9.17(h) x 0.36(d)

Table of Contents

Foundations of knowledge representation and reasoning.- Collective entities and relations in concept languages.- Computing extensions of terminological default theories.- A formalization of interval-based temporal subsumption in first order logic.- Normative, subjunctive and autoepistemic defaults.- Abductive reasoning with abstraction axioms.- Queries, rules and definitions as epistemic sentences in concept languages.- The power of beliefs or translating default logic into standard autoepistemic logic.- Learning an optimally accurate representation system.- Default reasoning via negation as failure.- Weak autoepistemic reasoning and well-founded semantics.- Forming concepts for fast inference.- A common-sense theory of time.- Reasoning with analogical representations.- Asking about possibilities — Revision and update semantics for subjunctive queries Extended report.- On the impact of stratification on the complexity of nonmonotonic reasoning.- Logics of mental attitudes in AI.- Hyperrational conditionals.- Revision by expansion in logic programs.