An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types

Authors

  • Stefano Albrecht The University of Edinburgh
  • Jacob Crandall Masdar Institute of Science and Technology
  • Subramanian Ramamoorthy The University of Edinburgh

DOI:

https://doi.org/10.1609/aaai.v29i1.9426

Keywords:

Prior Beliefs, Policy Types, Bayesian Games, Multiagent Systems

Abstract

Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

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Published

2015-02-18

How to Cite

Albrecht, S., Crandall, J., & Ramamoorthy, S. (2015). An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9426

Issue

Section

AAAI Technical Track: Multiagent Systems