Knowledge Representation, Reasoning, and the Design of Intelligent Agents

The Answer-Set Programming Approach

Nonfiction, Computers, Advanced Computing, Natural Language Processing, General Computing, Health & Well Being, Psychology
Cover of the book Knowledge Representation, Reasoning, and the Design of Intelligent Agents by Michael Gelfond, Yulia Kahl, Cambridge University Press
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Author: Michael Gelfond, Yulia Kahl ISBN: 9781107779464
Publisher: Cambridge University Press Publication: March 10, 2014
Imprint: Cambridge University Press Language: English
Author: Michael Gelfond, Yulia Kahl
ISBN: 9781107779464
Publisher: Cambridge University Press
Publication: March 10, 2014
Imprint: Cambridge University Press
Language: English

Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.

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Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.

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