A Game-theoretic Model and Best-response Learning Method for Ad Hoc Coordination in Multiagent Systems (Extended Abstract)
Stefano V. Albrecht, Subramanian Ramamoorthy · 2013
The ad hoc coordination problem is to design an ad hoc agent which is able to achieve optimal exibility and eciency 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-dened 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-dened 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 eciency but a signicantly higher welfare and winning rate.