2019/09/27 by Ran Tian, Nan Li, Tian, Ran +5
Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Computer Science and Game Theory (cs.GT) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #cs.AI #cs.GT #cs.LG
paper · pdf · doi:10.48550/arxiv.1909.12701
IEEE 2020 American Control Conference
openalex publication_date 2019/09/27 · openalex created_date 2019/10/03 · arxiv created 2021/02/13 · arxiv updated 2021/02/16 · openalex updated_date 2026/07/28
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.