General Game Playing
iyn 2022 · Springer Nature
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213
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General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun. But it is much more than that. It provides a theoretical framework for modeling discrete dynamic systems and defining rationality in a way that takes into account problem representation and complexities like incompleteness of information and resource bounds. It has practical applications in areas where these features are important, e.g., in business andlaw. More fundamentally, it raises questions about the nature of intelligence and serves as a laboratory in which to evaluate competing approaches to artificial intelligence. This book is an elementary introduction to General Game Playing (GGP). (1) It presents the theory of General Game Playing and leading GGP technologies. (2) It shows how to create GGP programs capable of competing against other programs and humans. (3) It offers a glimpse of some of the real-world applications of General Game Playing.
General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms...
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Michael Genesereth is an associate professor in the Computer Science Department at Stanford University. He received his Sc.B. in Physics from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. He is best known for his research on Computational Logic and its applications. He has been teaching Logic to Stanford students and others for more than 20 years. He is the current director of the Logic Group at Stanford and founder and research director of CodeX (The Stanford Center for Legal Informatics).Michael Thielscher is a Professor and head of the Computational Logic Group at Dresden University in Germany since 1997. He received his PhD in Computer Science from Darmstadt University of Technology, Germany. His research is mainly in Knowledge Representation, Cognitive Robotics, Commonsense Reasoning, Game Playing, and Constraint Logic Programming. He has developed the action programming language and system FLUX and has published numerous papers and two books on knowledge representation for actions, on comparisons of different action languages, and on implementations of action programming systems. In 1998, his Habilitation thesis was honored with the award for research excellence by the alumni of Darmstadt University of Technology. He co-authored the program FLUXPLAYER, which in 2006 was crowned the world champion at the Second General Game Playing Competition in Boston.
Michael Genesereth is an associate professor in the Computer Science Department at Stanford University. He received his Sc.B. in Physics from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. He is best known for his research on Computational Logic and its applications. He has ...
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