Matthew E. Taylor's Publications

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Towards a Theoretic Understanding of DCEE

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

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Abstract

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.

BibTeX Entry

@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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