TY - GEN
T1 - Modeling uncertainty in leading ad hoc teams
AU - Agmon, Noa
AU - Barrett, Samuel
AU - Stone, Peter
N1 - Publisher Copyright:
Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2014
Y1 - 2014
N2 - Ad hoc teamwork exists when a team of agents needs to cooperate without being able to communicate or use coordination schemes that were designed a-priori. Sometimes ad hoc teamwork amounts to acting so as to bring out the best in your teammates by "leading" them to the optimal joint action. Doing so can be challenging even when their behavior is fully known. In this paper, we take the challenge to the next level by considering the situation in which there is uncertainty about the teammates' behaviors. We discuss the problem of recursive modeling of the teammate's uncertain behavior in two-agent teams and conclude not only that the depth that is useful to model is bounded, but also the number of models useful to consider is linear in the number of actions (and not exponential, as expected). We then show that adopting a naive perspective might lead to negative long-term results in large teams, and thus introduce REACT, an algorithm for determining the action an agent should perform in order to maximize the team's expected utility. Finally, we show empirically that in randomly generated utility matrices, using REACT to select actions outperforms making incorrect assumptions about the identities of teammates.
AB - Ad hoc teamwork exists when a team of agents needs to cooperate without being able to communicate or use coordination schemes that were designed a-priori. Sometimes ad hoc teamwork amounts to acting so as to bring out the best in your teammates by "leading" them to the optimal joint action. Doing so can be challenging even when their behavior is fully known. In this paper, we take the challenge to the next level by considering the situation in which there is uncertainty about the teammates' behaviors. We discuss the problem of recursive modeling of the teammate's uncertain behavior in two-agent teams and conclude not only that the depth that is useful to model is bounded, but also the number of models useful to consider is linear in the number of actions (and not exponential, as expected). We then show that adopting a naive perspective might lead to negative long-term results in large teams, and thus introduce REACT, an algorithm for determining the action an agent should perform in order to maximize the team's expected utility. Finally, we show empirically that in randomly generated utility matrices, using REACT to select actions outperforms making incorrect assumptions about the identities of teammates.
KW - Agent cooperation
KW - Coalition formation
KW - Coordination
KW - Economic paradigms
KW - Game theory (cooperative and non-cooperative)
KW - Teamwork
UR - http://www.scopus.com/inward/record.url?scp=84911459254&partnerID=8YFLogxK
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AN - SCOPUS:84911459254
T3 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
SP - 397
EP - 404
BT - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Y2 - 5 May 2014 through 9 May 2014
ER -