A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems

Abstract

The ad hoc coordination problem is to design an ad hoc agent which is able to achieve optimal flexibility and efficiency in a multiagent system that admits no prior coordination between the ad hoc agent and the other agents. We conceptualise this problem formally as a stochastic Bayesian game in which the behaviour of a player is determined by its type. Based on this model, we derive a solution, called Harsanyi-Bellman Ad Hoc Coordination (HBA), which utilises a set of user-defined types to characterise players based on their observed behaviours. We evaluate HBA in the level-based foraging domain, showing that it outperforms several alternative algorithms using just a few user-defined types. We also report on a human-machine experiment in which the humans played Prisoner's Dilemma and Rock-Paper-Scissors against HBA and alternative algorithms. The results show that HBA achieved equal efficiency but a significantly higher welfare and winning rate.