Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning
This addresses the challenge of AI decision-making in non-numerical tasks, but it is incremental as it applies known theories to a specific game.
The researchers tackled the problem of AI outperforming humans in decision-making by developing an algorithm for the penny-matching game, which beat 27 out of 30 human players in experiments.
It is a long-standing goal of artificial intelligence (AI) to be superior to human beings in decision making. Games are suitable for testing AI capabilities of making good decisions in non-numerical tasks. In this paper, we develop a new AI algorithm to play the penny-matching game considered in Shannon's "mind-reading machine" (1953) against human players. In particular, we exploit cognitive hierarchy theory and Bayesian learning techniques to continually evolve a model for predicting human player decisions, and let the AI player make decisions according to the model predictions to pursue the best chance of winning. Experimental results show that our AI algorithm beats 27 out of 30 volunteer human players.