Online Planning for Ad Hoc Autonomous Agent Teams

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We propose a novel online planning algorithm for ad hoc team settings' challenging situations in which an agent must collaborate with unknown teammates without prior coordination. Our approach is based on constructing and solving a series of stage games, and then using biased adaptive play to choose actions. The utility function in each stage game is estimated via Monte-Carlo tree search using the UCT algorithm. We establish analytically the convergence of the algorithm and show that it performs well in a variety of ad hoc team domains.

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@inproceedings{WZCijcai11,
 address = {Barcelona, Spain},
 author = {Feng Wu and Shlomo Zilberstein and Xiaoping Chen},
 booktitle = {Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI)},
 doi = {10.5591/978-1-57735-516-8/IJCAI11-081},
 pages = {439-445},
 title = {Online Planning for Ad Hoc Autonomous Agent Teams},
 year = {2011}
}