Abstract
Cognitive models of blackjack playing are presented and investigated. Blackjack playing is considered a useful test case for theories on human learning. Curiously, despite the existence of a relatively simple, well-known and optimal strategy for blackjack, empirical studies have found that casino players play quite differently from that strategy. The computational models presented here attempt to explain this result by modelling blackjack playing using the cognitive architecture CHREST. Two approaches to modeling are investigated and compared; (i) the combination of classical and operant conditioning, as studied in psychology, and (ii) SARSA, as studied in AI.
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© 2012 Springer-Verlag Berlin Heidelberg
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Schiller, M.R.G., Gobet, F.R. (2012). A Comparison between Cognitive and AI Models of Blackjack Strategy Learning. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_13
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DOI: https://doi.org/10.1007/978-3-642-33347-7_13
Publisher Name: Springer, Berlin, Heidelberg
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