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Multiagent teamwork: analyzing the optimality and complexity of key theories and models
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Source International Conference on Autonomous Agents archive
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2 table of contents
Bologna, Italy
SESSION: Session 7C: theories of agency, autonomy, and papers table of contents
Pages: 873 - 880  
Year of Publication: 2002
ISBN:1-58113-480-0
Authors
David V. Pynadath  University of Southern California, Marina del Rey, CA
Milind Tambe  University of Southern California, Marina del Rey, CA
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 12,   Downloads (12 Months): 77,   Citation Count: 18
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ABSTRACT

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Thus, we cannot determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP) model, which is general enough to subsume many existing models of multiagent systems. We analyze use the COM-MTDP model to provide a breakdown of the computational complexity of constructing optimal teams under problem domains divided along the dimensions of observability and communication cost. We then exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory, including STEAM. We then derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations. We have implemented a reusable, domain-independent software package based COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.


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.

 
1
 
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P.R. Cohen & H.J. Levesque. Teamwork. Nous, 25(4):487--512, 1991
 
4
D. Goldberg & M.J. Matarićć. Interference as a tool for designing & evaluating multi-robot controllers. AAAI, 637--642, 1997
 
5
 
6
Y.-C. Ho. Team decision theory & information structures. Proc. of the IEEE, 68(6):644--654, 1980
 
7
 
8
J. Marschak & R. Radner. The Econ. Theory of Teams. Yale, 1971
 
9
10
 
11
R.D. Smallwood & E.J. Sondik. The optimal control of POMDPs over a finite horizon. Operations Research, 21:1071--1088, 1973
 
12
I.A. Smith & P.R. Cohen. Toward a semantics for an ACL based on speech-acts. AAAI, 24--31, 1996
 
13
E. Sonenberg, G. Tidhar, E. Werner, D. Kinny, M. Ljungberg, & A. Rao. Planned team activity. Tech. Rep. 26, Austral. AI Inst., 1994
 
14
M. Tambe. Towards flexible teamwork. JAIR, 7:83--124, 1997
 
15
16
 
17
J. Yen, J. Yin, T.R. Ioerger, M.S. Miller, D. Xu, & R.A. Volz. CAST: Collab. agents for simulating teamwork. IJCAI, 1135--1142, 2001

CITED BY  18

Collaborative Colleagues:
David V. Pynadath: colleagues
Milind Tambe: colleagues