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It is a long-standing goal of artificial intelligence (AI) to be superior tohuman beings in decision making. Games are suitable for testing AI capabilitiesof making good decisions in non-numerical tasks. In this paper, we develop anew AI algorithm to play the penny-matching game considered in Shannon's"mind-reading machine" (1953) against human players. In particular, we exploitcognitive hierarchy theory and Bayesian learning techniques to continuallyevolve a model for predicting human player decisions, and let the AI playermake decisions according to the model predictions to pursue the best chance ofwinning. Experimental results show that our AI algorithm beats 27 out of 30volunteer human players.

Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning