Title
Knowledgefree And Learningbased Methods In Intelligent Game Playing (Studies In Computational Intelligence, 276)
Delivery time: 8-12 business days (International)
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 Brutefor Type Search, Use Unlimited Memory Resources, But At The Same Time Are Poor At Using Reasoningbased Approaches And Abstractionbased 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 Humancompatible 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 Humantype 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.
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