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Scott Alfeld, Matthew E. Taylor, Prateek Tandon, and Milind
Tambe. Towards a Theoretic Understanding of DCEE. In Proceedings of the Distributed Constraint Reasoning workshop
(at AAMAS-10), May 2010.
DCR-10
Common wisdom says that the greater the level of teamwork, the higherthe performance of the team. In teams of cooperative
autonomousagents, working together rather than independently can increase theteam reward. However, recent results show that
in uncertainenvironments, increasing the level of teamwork can actually decreaseoverall performance. Coined the team uncertainty
penalty, thisphenomenon has been shown empirically in simulation, but theunderlying mathematics are not yet understood. By
understanding themathematics, we could develop algorithms that reduce or eliminate thispenalty of increased teamwork.
In
this paper we investigate the team uncertainty penalty on twofronts. First, we provide results of robots exhibiting the samebehavior
seen in simulations. Second, we present a mathematicalfoundation by which to analyze the phenomenon. Using this model, wepresent
findings indicating that the team uncertainty penalty isinherent to the level of teamwork allowed, rather than to specificalgorithms.
@inproceedings(DCR10-Alfeld, author="Scott Alfeld and Matthew E.\ Taylor and Prateek Tandon and Milind Tambe", title="Towards a Theoretic Understanding of {DCEE}", Booktitle="Proceedings of the Distributed Constraint Reasoning workshop (at AAMAS-10)", month="May", year= "2010", wwwnote={<a href="https://www.cs.drexel.edu/dcr2010">DCR-10</a>}, abstract={ Common wisdom says that the greater the level of teamwork, the higher the performance of the team. In teams of cooperative autonomous agents, working together rather than independently can increase the team reward. However, recent results show that in uncertain environments, increasing the level of teamwork can actually decrease overall performance. Coined the team uncertainty penalty, this phenomenon has been shown empirically in simulation, but the underlying mathematics are not yet understood. By understanding the mathematics, we could develop algorithms that reduce or eliminate this penalty of increased teamwork. <br> In this paper we investigate the team uncertainty penalty on two fronts. First, we provide results of robots exhibiting the same behavior seen in simulations. Second, we present a mathematical foundation by which to analyze the phenomenon. Using this model, we present findings indicating that the team uncertainty penalty is inherent to the level of teamwork allowed, rather than to specific algorithms. }, )
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