Fr. 135.00

Knowledge-Free and Learning-Based Methods in Intelligent Game Playing

English · Paperback / Softback

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Description

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Humans and machines are very di?erent in their approaches to game pl- ing. Humans use intuition, perception mechanisms, selective search, creat- ity, abstraction, heuristic abilities and other cognitive skills to compensate their (comparably) slow information processing speed, relatively low m- ory capacity, and limited search abilities. Machines, on the other hand, are extremely fast and infallible in calculations, capable of e?ective brute-for- type search, use "unlimited" memory resources, but at the same time are poor at using reasoning-based approaches and abstraction-based methods. The above major discrepancies in the human and machine problem solving methods underlined the development of traditional machine game playing as being focused mainly on engineering advances rather than cognitive or psychological developments. In other words, as described by Winkler and F¨ urnkranz [347, 348] with respect to chess, human and machine axes of game playing development are perpendicular, but the most interesting, most promising, and probably also most di?cult research area lies on the junction between human-compatible knowledge and machine compatible processing.I undoubtedly share this point of view and strongly believe that the future of machine game playing lies in implementation of human-type abilities (- straction,intuition,creativity,selectiveattention,andother)whilestilltaking advantage of intrinsic machine skills. Thebookisfocusedonthedevelopmentsandprospectivechallengingpr- lems in the area of mind gameplaying (i.e. playinggames that require mental skills) using Computational Intelligence (CI) methods, mainly neural n- works, genetic/evolutionary programming and reinforcement learning.

List of contents

I: AI Tools and State-of-the-Art Accomplishments in Mind Games.- Foundations of AI and CI in Games. Claude Shannon's Postulates.- Basic AI Methods and Tools.- State of the Art.- II: CI Methods in Mind Games. Towards Human-Like Playing.- An Overview of Computational Intelligence Methods.- CI in Games - Selected Approaches.- III: An Overview of Challenges and Open Problems.- Evaluation Function Learning.- Game Representation.- Efficient TD Training.- Move Ranking and Search-Free Playing.- Modeling the Opponent and Handling the Uncertainty.- IV: Grand Challenges.- Intuition.- Creativity and Knowledge Discovery.- Multi-game Playing.- Summary and Perspectives.

Summary

Humans and machines are very di?erent in their approaches to game pl- ing. Humans use intuition, perception mechanisms, selective search, creat- ity, abstraction, heuristic abilities and other cognitive skills to compensate their (comparably) slow information processing speed, relatively low m- ory capacity, and limited search abilities. Machines, on the other hand, are extremely fast and infallible in calculations, capable of e?ective brute-for- type search, use “unlimited” memory resources, but at the same time are poor at using reasoning-based approaches and abstraction-based methods. The above major discrepancies in the human and machine problem solving methods underlined the development of traditional machine game playing as being focused mainly on engineering advances rather than cognitive or psychological developments. In other words, as described by Winkler and F¨ urnkranz [347, 348] with respect to chess, human and machine axes of game playing development are perpendicular, but the most interesting, most promising, and probably also most di?cult research area lies on the junction between human-compatible knowledge and machine compatible processing.I undoubtedly share this point of view and strongly believe that the future of machine game playing lies in implementation of human-type abilities (- straction,intuition,creativity,selectiveattention,andother)whilestilltaking advantage of intrinsic machine skills. Thebookisfocusedonthedevelopmentsandprospectivechallengingpr- lems in the area of mind gameplaying (i.e. playinggames that require mental skills) using Computational Intelligence (CI) methods, mainly neural n- works, genetic/evolutionary programming and reinforcement learning.

Product details

Authors Jacek Mandziuk
Publisher Springer, Berlin
 
Languages English
Product format Paperback / Softback
Released 08.02.2012
 
EAN 9783642262135
ISBN 978-3-642-26213-5
No. of pages 254
Weight 423 g
Illustrations XVIII, 254 p. 29 illus.
Series Studies in Computational Intelligence
Studies in Computational Intelligence
Subjects Natural sciences, medicine, IT, technology > IT, data processing > IT

B, Artificial Intelligence, Modeling, Learning, engineering, Reinforcement Learning, Computational Intelligence, knowledge discovery, neural network

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