Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions. © 2013 Springer-Verlag.
CITATION STYLE
Pippin, C. E., & Christensen, H. (2013). Learning task performance in market-based task allocation. In Advances in Intelligent Systems and Computing (Vol. 194 AISC, pp. 613–621). Springer Verlag. https://doi.org/10.1007/978-3-642-33932-5_57
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