A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems

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Abstract / Description of output

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.
Original languageEnglish
Title of host publicationAAMAS '13 Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages1155-1156
Number of pages2
ISBN (Print)978-1-4503-1993-5
Publication statusPublished - 2013

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