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

TL;DR

This paper presents an AI algorithm that outperforms most human players in a penny-matching game by using cognitive hierarchy theory and Bayesian learning to predict human decisions.

Contribution

The paper introduces a novel AI approach combining cognitive hierarchy theory and Bayesian learning to improve decision-making in non-numerical games.

Findings

AI beats 27 out of 30 human players

The model effectively predicts human decision patterns

The approach advances AI capabilities in game-playing scenarios

Abstract

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.