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

Mandziuk, Jacek

ISBN 10: 3642116779 / ISBN 13: 9783642116773

Published by Springer, Berlin, 2010

About this Item

First Edition. Hardcover. 9 1/2" X 6 1/2". xviii, 254pp. First edition, with full number line indicating first printing. Very mild shelf wear to covers and edges of paper over boards. Bumps to corners. Pages are clean and unmarked. Binding is firm, tight, and sound. ABOUT THIS BOOK: Humans and machines are very dfferent in their approaches to game playing. Humans use intuition, perception mechanisms, selective search, creativity, abstraction, heuristic abilities and other cognitive skills to compensate their (comparably) slow information processing speed, relatively low memory capacity, and limited search abilities. Machines, on the other hand, are extremely fast and infallible in calculations, capable of effective brute-force 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 diffcult 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 (abstraction, intuition, creativity, selective attention, and others) while still taking advantage of intrinsic machine skills.(Publisher).

Seller Inventory # 11509

Bibliographic Details

• Title: Knowledge-Free and Learning-Based Methods in Intelligent Game Playing
• Publisher: Springer, Berlin
• Publication Date: 2010
• Language: English
• ISBN 10: 3642116779
• ISBN 13: 9783642116773
• Binding: Hardcover
• Edition: First Edition.
• Condition: Very good +
• Seller catalogs:
  • Psychology
  • Games

About this title

Synopsis

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.

From the Back Cover

The book is focused on the developments and prospective challenging problems in the area of mind game playing (i.e. playing games that require mental skills) using Computational Intelligence (CI) methods, mainly neural networks, genetic/evolutionary programming and reinforcement learning. The majority of discussed game playing ideas were selected based on their functional similarity to human game playing. These similarities include: learning from scratch, autonomous experience-based improvement and example-based learning. The above features determine the major distinction between CI and traditional AI methods relying mostly on using effective game tree search algorithms, carefully tuned hand-crafted evaluation functions or hardware-based brute-force methods.

On the other hand, it should be noted that the aim of this book is by no means to underestimate the achievements of traditional AI methods in game playing domain. On the contrary, the accomplishments of AI approaches are undisputable and speak for themselves. The goal is rather to express my belief that other alternative ways of developing mind game playing machines are possible and urgently needed.