000 04107nam a22005295i 4500
001 978-3-031-01569-4
003 DE-He213
005 20240730163630.0
007 cr nn 008mamaa
008 220601s2014 sz | s |||| 0|eng d
020 _a9783031015694
_9978-3-031-01569-4
024 7 _a10.1007/978-3-031-01569-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGenesereth, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979624
245 1 0 _aGeneral Game Playing
_h[electronic resource] /
_cby Michael Genesereth, Michael Thielscher.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXVI, 213 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Introduction -- Game Description -- Game Management -- Game Playing -- Small Single-Player Games -- Small Multiple-Player Games -- Heuristic Search -- Probabilistic Search -- Propositional Nets -- General Game Playing With Propnets -- Factoring -- Discovery of Heuristics -- Logic -- Analyzing Games with Logic -- Solving Single-Player Games with Logic -- Discovering Heuristics with Logic -- Games with Incomplete Information -- Games with Historical Constraints -- Incomplete Game Descriptions -- Advanced General Game Playing -- Authors' Biographies.
520 _aGeneral 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979625
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aThielscher, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979626
710 2 _aSpringerLink (Online service)
_979627
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004414
776 0 8 _iPrinted edition:
_z9783031026973
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979628
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01569-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c84815
_d84815