Adaptive multi-agent decision making using analytical hierarchy process

Juei Nan Chen, Yueh-Min Huang, William C. Chu

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

2 Citations (Scopus)

Abstract

In multi-agent decision-making problems, we should treat all the participating agents with no partiality. In this paper, we seek to elicit the cooperation level from each agent's inner world. This benefit would be gained by the reasonable preference values to each alternative in the viewpoint of the group. Besides, we propose a methodology to adapt group's preference functions. It can make all the participating agents have the chances to pick up the most favorite choice after several rounds of decision-making process.

Original languageEnglish
Title of host publicationAI 2002
Subtitle of host publicationAdvances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings
EditorsBob McKay, John Slaney
PublisherSpringer Verlag
Pages203-212
Number of pages10
ISBN (Print)3540001972, 9783540001973
Publication statusPublished - 2002 Jan 1
Event15th Australian Joint Conference on Artificial Intelligence, AI 2002 - Canberra, Australia
Duration: 2002 Dec 22002 Dec 6

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2557
ISSN (Print)0302-9743

Other

Other15th Australian Joint Conference on Artificial Intelligence, AI 2002
CountryAustralia
CityCanberra
Period02-12-0202-12-06

Fingerprint

Analytical Hierarchy Process
Decision making
Decision Making
Methodology
Alternatives

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chen, J. N., Huang, Y-M., & Chu, W. C. (2002). Adaptive multi-agent decision making using analytical hierarchy process. In B. McKay, & J. Slaney (Eds.), AI 2002: Advances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 203-212). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2557). Springer Verlag.
Chen, Juei Nan ; Huang, Yueh-Min ; Chu, William C. / Adaptive multi-agent decision making using analytical hierarchy process. AI 2002: Advances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings. editor / Bob McKay ; John Slaney. Springer Verlag, 2002. pp. 203-212 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).
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Chen, JN, Huang, Y-M & Chu, WC 2002, Adaptive multi-agent decision making using analytical hierarchy process. in B McKay & J Slaney (eds), AI 2002: Advances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2557, Springer Verlag, pp. 203-212, 15th Australian Joint Conference on Artificial Intelligence, AI 2002, Canberra, Australia, 02-12-02.

Adaptive multi-agent decision making using analytical hierarchy process. / Chen, Juei Nan; Huang, Yueh-Min; Chu, William C.

AI 2002: Advances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings. ed. / Bob McKay; John Slaney. Springer Verlag, 2002. p. 203-212 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2557).

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

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Chen JN, Huang Y-M, Chu WC. Adaptive multi-agent decision making using analytical hierarchy process. In McKay B, Slaney J, editors, AI 2002: Advances in Artificial Intelligence - 15th Australian Joint Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2002. p. 203-212. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).