Readings in Knowledge Representation
Ronald J. Brachman, Hector J. Levesque
M. Kaufmann Publishers, 1985 - Reference - 571 pages
In Artificial Intelligence, it is often said that the representation of knowledge is the key to the design of robust intelligent systems. In one form or another the principles of Knowledge Representation are fundamental to work in natural language processing, computer vision, knowledge-based expert systems, and other areas. The papers reprinted in this volume have been collected to allow the reader with a general technical background in AI to explore the subtleties of this key subarea. These seminal articles, spanning a quarter-century of research, cover the most important ideas and developments in the representation field. The editors introduce each paper, discuss its relevance and context, and provide an extensive bibliography of other work. "Readings in Knowledge Representation" is intended to serve as a complete sourcebook for the study of this crucial subject.
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Some Problems and NonProblems in Representation Theory
Epistemological Problems of Artificial Intelligence
Prologue to Reflection and Semantics in a Procedural Language
26 other sections not shown
ACTOR advice taker algorithm Artificial Intelligence assertion axioms believe Bobrow Brachman causal closed world assumption Cognitive Cognitive Science comparison note concept Conceptual Dependency context corresponding deduction default defined denote described descriptor diagram discussed domain entities example explicit expressions fact first-order logic formal frame Fregean function goal Hayes hierarchy human idea implemented individual inference interpretation intervals John kind Knowledge Representation KRYPTON logic machine MARY match meaning mechanism memory metatheory Minsky modal logic model theory natural language node notation notion object operations paper particular phone number pointer possible predicate calculus primitive problem problem-solving Proc procedures programming language properties proposition question Quillian reasoning reference relations relationships represent representation language retina role rules Schank semantic network sentence simple situation specific symbols TBox theorem theory things tion true understanding variables Winograd word
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