Knowledge representation, reasoning, and the design of intelligent agents : the answer-set programming approach / Michael Gelfond, Texas Tech University, Yulia Kahl.
By: Gelfond, Michael [author.]
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Contributor(s): Kahl, Yulia [author.].
Material type: 
BookPublisher: Cambridge : Cambridge University Press, 2014Description: 1 online resource (xiv, 348 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781139342124 (ebook).Other title: Knowledge Representation, Reasoning, & the Design of Intelligent Agents.Subject(s): Intelligent agents (Computer software)Additional physical formats: Print version: : No titleDDC classification: 006.3 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
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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