Developing game-playing agents that adapt to user strategies: A case study

This paper describes the development of a novel web-delivered computer game, Boundary, where human players vie against each other or computer agents that use adaptive learning to modify playing strategies. The novelty presents challenges in game development both in terms of game playability and enjoyment as well as designing intelligent computer game players. An adaptive artificial intelligent agent was developed by creating several basic AI agents, each of which employs a unique, simple strategy. The adaptive agent classifies its opponent's play during the game by simulating what moves each simple strategy would make and identifying the strategy that produces the closest approximation to the opponent's actions. During development, through computer-computer simulations, the relative strengths of each strategy versus the others were determined. Thus, once an opponent's moves are matched to the closest known strategy, the best counter-strategy can be selected by the computer agent. Our hypotheses are that 1) humans will quickly learn how to counter the static AI strategies, 2) humans will have more difficulty learning how to counter the adaptive AI, and 3) human players will judge the adaptive player as more challenging. This paper describes human subject experiments to test those hypotheses.