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The role of Learning in systems of reusable heterogeneous design agents

Published online by Cambridge University Press:  27 February 2009

M. V. Nagendra Prasad ,
Susan E. Lander  and
Victor R. Lesser
M. V. Nagendra Prasad
Affiliation:
Department of Computer Science, University of Massachusetts, Amherst, MA 01003, U.S.A.
Susan E. Lander
Affiliation:
Blackboard Technology Group, Inc., 401 Main Street, Amherst, MA 01002, U.S.A.
Victor R. Lesser
Affiliation:
Department of Computer Science, University of Massachusetts, Amherst, MA 01003, U.S.A.
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Extract

In this abstract, we discuss the use of learning techniques to improve performance and solution quality in multiagent parametric design. We have implemented the L-TEAM testbed for empirical evaluation of two forms of learning (described in detail below):

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 163 - 165
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Prasad, M.V.N., Lesser, V.R., & Lander, S.E. (1995a). Learning organizational roles in a heterogeneous multi-agent system. Technical report TR 95–35, Computer Science Department, University of Massachusetts.Google Scholar
Prasad, M.V.N., Lesser, V.R., & Lander, S.E. (1995b). Learning experiments in a heterogeneous multi-agent system. Proc. Workshop on Adaptation and Learn. Multi-agent Syst., IJCAI-95.Google Scholar
Prasad, M.V.N., Lander, S.E., & Lesser, V.R. (1996). Cooperative learning over composite search spaces: Experiences with a multi-agent design system. Technical report, Computer Science Department, University of Massachusetts, Amherst.Google Scholar
Lander, S.E., Lesser, V.R. (1993). Understanding the role of negotiation in distributed search among heterogeneous agents. Proc. Int. Joint Conf. Artif. Intell.Google Scholar
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Lander, S.E., & Lesser, V.R. (in press). Sharing meta-information to guide cooperative search among heterogeneous reusable agents. IEEE Trans. Knowl, Data Eng.Google Scholar
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