| Conflicts in teamwork: hybrids to the rescue |
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International Conference on Autonomous Agents
archive
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
table of contents
The Netherlands
Pages: 3 - 10
Year of Publication: 2005
ISBN:1-59593-093-0
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Authors
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M. Tambe
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University of Southern California, Los Angeles, CA
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E. Bowring
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University of Southern California, Los Angeles, CA
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H. Jung
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Institute for Human-Machine Cognition, Pensacola, FL
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G. Kaminka
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Bar Ilan University, Ramat Gan, Israel
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R. Maheswaran
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University of Southern California, Los Angeles, CA
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J. Marecki
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University of Southern California, Los Angeles, CA
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P. J. Modi
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Carnegie Mellon University, Pittsburgh, PA
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R. Nair
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Honeywell Laboratories, Minneapolis, MN
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S. Okamoto
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Carnegie Mellon University, Pittsburgh, PA
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J. P. Pearce
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University of Southern California, Los Angeles, CA
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P. Paruchuri
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University of Southern California, Los Angeles, CA
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D. Pynadath
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University of Southern California, Los Angeles, CA
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P. Scerri
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Carnegie Mellon University, Pittsburgh, PA
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N. Schurr
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University of Southern California, Los Angeles, CA
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P. Varakantham
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University of Southern California, Los Angeles, CA
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Downloads (6 Weeks): 20, Downloads (12 Months): 91, Citation Count: 3
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 ABSTRACT
Today within the AAMAS community, we see at least four competing approaches to building multiagent systems: belief-desire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic approaches. While there is exciting progress within each approach, there is a lack of cross-cutting research. This paper highlights hybrid approaches for multiagent teamwork. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed POMDPs in building agent teams. While DCOP and distributed POMDP algorithms provide promising results, hybrid approaches help us address problems of scalability and expressiveness. For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance. We present some recent results from applying this approach in a Disaster Rescue simulation domain being developed with help from the Los Angeles Fire Department.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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E. Bowring, M. Tambe, and M. Yokoo. Distributed multi-criteria coordination in multi-agent systems. In Workshop on DALT, 2005.
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4
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5
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G. Kaminka, D. V. Pynadath, and M. Tambe. Monitoring teams by overhearing: A multi-agent plan recognition approach. JAIR, 17, 2002.
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6
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G. Kaminka and M. Tambe. Robust multi-agent teams via socially-attentive monitoring. JAIR, 12:105--147, 2000.
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7
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8
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H. Levesque, P. R. Cohen, and J. H. T. Nunes. On acting together. In AAAI, 1990.
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9
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R. Maheswaran, J. Pearce, P. Varakantham, E. Bowring, and M. Tambe. Valuation of possible states: A unifying quantitative framework for evaluating privacy in collaboration. In AAMAS, 2005.
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R. Maheswaran, J. P. Pearce, and M. Tambe. Distributed algorithms for DCOP: A graphical-game-based approach. In PDCS, 2004.
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R. Nair and M. Tambe. Hybrid bdi-pomdp framework for multiagent teaming. JAIR, 23:367--413, 2005.
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R. Nair, M. Tambe, M. Yokoo, D. Pynadath, and S. Marsella. Taming decentralized pomdps: Towards efficient policy computation for multiagent settings. In IJCAI, 2003.
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R. Nair, P. Varakantham, M. Yokoo, and M. Tambe. Networked distributed pomdps: A synergy of distributed constraint optimization and pomdps. In IJCAI, 2005.
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Praveen Paruchuri , Milind Tambe , Fernando Ordonez , Sarit Kraus, Towards a Formalization of Teamwork with Resource Constraints, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, p.596-603, July 19-23, 2004, New York, New York
[doi> 10.1109/AAMAS.2004.277]
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J. Pearce, R. T. Maheswaran, and M. Tambe. Dcop games for multi-agent coordination. In Workshop on DCR, 2005.
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21
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22
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23
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Paul Scerri , David Pynadath , Lewis Johnson , Paul Rosenbloom , Mei Si , Nathan Schurr , Milind Tambe, A prototype infrastructure for distributed robot-agent-person teams, Proceedings of the second international joint conference on Autonomous agents and multiagent systems, July 14-18, 2003, Melbourne, Australia
[doi> 10.1145/860575.860645]
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P. Scerri, D. Pynadath, and M. Tambe. Towards adjustable autonomy for the real-world. JAIR, 17:171--228, 2002.
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26
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27
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M. Tambe. Towards flexible teamwork. JAIR, 7:83--124, 1997.
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29
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30
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M. Tambe, W. Johnson, R. Jones, F. Koss, J. Laird, P. Rosenbloom, and K. Schwamb. Intelligent agents for interactive simulation environments. AI Magazine, page 16(1), 1995.
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31
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32
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33
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J. Yen, J. Yin, T. R. Ioerger, M. S. Miller, D. Xu, and R. A. Volz. Cast: Collaborative agents for simulating teamwork. In IJCAI, 2001.
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