Knowledge-Free and Learning-Based Methods in Intelligent Game Playing: 276 - Rilegato

Mandziuk, Jacek

Rilegato

ISBN 10: 3642116779 ISBN 13: 9783642116773

Casa editrice: Springer-Nature New York Inc, 2010

Riassunto

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 3642116779
• ISBN 13: 9783642116773
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 254
• Contatto del produttore: Springer-Verlag GmbH; customerservice@springernature.com; ; Tiergartenstraße 17; Heidelberg; Baden-Württemberg; 69121; Germania