Learning task performance in market-based task allocation

© 2012 Springer-Verlag. The original publication is available at www.springerlink.com.Presented at the 12th International Conference on Intelligent Autonomous Systems (IAS-12) held June 26-29, 2012, Jeju Island, Korea.DOI: 10.1007/978-3-642-33932-5_57Auction 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