Learning how to plan and instantiate a plan in multi-agent coalition formation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We propose an innovative two-step learning approach to planning- instantiation for multi-agent coalition formation in dynamic, uncertain, real-time, and noisy environments. The first step teams about the planning of a coalition to improve its quality, adapting to the real-time and environmental requirements. The second step learns about the instantiation of the plan to improve the formation process, taking into account uncertain and dynamic behaviors of the peer agents. Decomposing the approach into two steps allows for modularity and flexibility in learning: learning how to plan a coalition is strategic while learning how to instantiate a plan is tactical. Our approach employs a case-based reinforcement learning (CBRL) framework.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Systems. IAT 2004
EditorsN. Zhong, J. Bradshaw, S.K. Pal, D. Talia, J. Liu, N. Cercone
Pages479-482
Number of pages4
StatePublished - 2004
EventProceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IAT 2004 - Beijing, China
Duration: Sep 20 2004Sep 24 2004

Publication series

NameProceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IAT 2004

Conference

ConferenceProceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IAT 2004
CountryChina
CityBeijing
Period9/20/049/24/04

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Li, X., & Soh, L. K. (2004). Learning how to plan and instantiate a plan in multi-agent coalition formation. In N. Zhong, J. Bradshaw, S. K. Pal, D. Talia, J. Liu, & N. Cercone (Eds.), Proceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Systems. IAT 2004 (pp. 479-482). (Proceedings - IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IAT 2004).