A comparison between cognitive and AI models of blackjack strategy learning
Schiller, M. R. & 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 - 35th Annual German Conference on AI, Proceedings
(pp. 143 - 155).
Springer Berlin / Heidelberg.
https://doi.org/10.1007/978-3-642-33347-7_13
(2012).
A comparison between cognitive and AI models of blackjack strategy learning.
In
Glimm, B. & Krüger, A.
(Eds.),
KI 2012: Advances in Artificial Intelligence - 35th Annual German Conference on AI, Proceedings
(pp. 143 - 155).
Springer Berlin / Heidelberg.
https://doi.org/10.1007/978-3-642-33347-7_13
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.