Congregation Formation in Multiagent Systems
dc.contributor.author | Brooks, Christopher H. | en_US |
dc.contributor.author | Durfee, Edmund H. | en_US |
dc.date.accessioned | 2006-09-11T14:10:58Z | |
dc.date.available | 2006-09-11T14:10:58Z | |
dc.date.issued | 2003-07 | en_US |
dc.identifier.citation | Brooks, Christopher H.; Durfee, Edmund H.; (2003). "Congregation Formation in Multiagent Systems." Autonomous Agents and Multi-Agent Systems 7 (1-2): 145-170. <http://hdl.handle.net/2027.42/44028> | en_US |
dc.identifier.issn | 1387-2532 | en_US |
dc.identifier.issn | 1573-7454 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44028 | |
dc.description.abstract | We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs in large-scale MAS. When agents must search for other agents to interact with, congregations provide a way for agents to bias this search towards groups of agents that have tended to produce successful interactions in the past. This causes each agent's search problem to scale with the size of a congregation rather than the size of the population as a whole. In this paper, we present a formal model of a congregation and then apply Vidal and Durfee's CLRI framework [24] to the congregating problem. We apply congregating to the affinity group domain, and show that if agents are unable to describe congregations to each other, the problem of forming optimal congregations grows exponentially with the number of agents. The introduction of labelers provides a means of coordinating agent decisions, thereby reducing the problem's complexity. We then show how a structured label space can be exploited to simplify the labeler's decision problem and make the congregating problem linear in the number of labels. We then present experimental evidence demonstrating how congregating can be used to reduce agents' search costs, thereby allowing the system to scale up. We conclude with a comparison to other methods for coordinating multiagent behavior, particularly teams and coalitions. | en_US |
dc.format.extent | 255945 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Software Engineering/Programming and Operating Systems | en_US |
dc.subject.other | Data Structures, Cryptology and Information Theory | en_US |
dc.subject.other | User Interfaces and Human Computer Interaction | en_US |
dc.subject.other | Artificial Intelligence (Incl. Robotics) | en_US |
dc.subject.other | Coordinating Multiple Agents | en_US |
dc.subject.other | Scalability and Complexity Issues | en_US |
dc.subject.other | Multiagent Learning | en_US |
dc.title | Congregation Formation in Multiagent Systems | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Artificial Intelligence Laboratory, University of Michigan, Ann Arbor, MI, 48109 | en_US |
dc.contributor.affiliationother | Computer Science Department, University of San Francisco, San Francisco, CA, 94117-1080 | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44028/1/10458_2004_Article_5124857.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1024133006761 | en_US |
dc.identifier.source | Autonomous Agents and Multi-Agent Systems | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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