Uncertainty in Artificial Intelligence
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On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Stefano Albrecht, Subramanian Ramamoorthy
While many multiagent algorithms are designed for homogeneous systems (i.e. all agents are iden- tical), there are important applications which re- quire an agent to coordinate its actions without knowing a priori how the other agents behave. One method to make this problem feasible is to as- sume that the other agents draw their latent policy (or type) from a specific set, and that a domain ex- pert could provide a specification of this set, albeit only a partially correct one. Algorithms have been proposed by several researchers to compute poste- rior beliefs over such policy libraries, which can then be used to determine optimal actions. In this paper, we provide theoretical guidance on two cen- tral design parameters of this method: Firstly, it is important that the user choose a posterior which can learn the true distribution of latent types, as otherwise suboptimal actions may be chosen. We analyse convergence properties of two existing posterior formulations and propose a new poste- rior which can learn correlated distributions. Sec- ondly, since the types are provided by an expert, they may be inaccurate in the sense that they do not predict the agentsā?? observed actions. We pro- vide a novel characterisation of optimality which allows experts to use efficient model checking al- gorithms to verify optimality of types.
Pages: 12-21
PS Link:
PDF Link: /papers/14/p12-albrecht.pdf
AUTHOR = "Stefano Albrecht and Subramanian Ramamoorthy",
TITLE = "On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "12--21"

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