Revenue maximising adaptive auctioneer agent

Janine Claire Pike; Elizabeth Marie Ehlers

doi:10.1007/978-3-540-89674-6_38

Revenue maximising adaptive auctioneer agent

Janine Claire Pike, Elizabeth Marie Ehlers

Academy of Computer Science and Software Engineering

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Auction theory has proven that auction revenue is influenced by factors such as the auction format and the auction parameters. The Revenue Maximising Adaptive Auctioneer (RMAA) agent model has been developed with the aim of generating maximum auction revenue by adapting the auction format and parameters to suit the auction environment. The RMAA agent uses a learning classifier system to learn which rules are profitable in a particular bidding environment. The profitable rules are then exploited by the RMAA agent to generate maximum revenue. The RMAA agent model can effectively adapt to a real time dynamic auction environment.

Original language	English
Title of host publication	Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings
Pages	340-347
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-540-89674-6_38
Publication status	Published - 2008
Event	11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008 - Hanoi, Viet Nam Duration: 15 Dec 2008 → 16 Dec 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5357 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008
Country/Territory	Viet Nam
City	Hanoi
Period	15/12/08 → 16/12/08

Keywords

Agent auctions
Auction theory
Learning classifier system
Reinforcement learning
ZCS

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-89674-6_38

Cite this

Pike, J. C., & Ehlers, E. M. (2008). Revenue maximising adaptive auctioneer agent. In Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings (pp. 340-347). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5357 LNAI). https://doi.org/10.1007/978-3-540-89674-6_38

Pike, Janine Claire ; Ehlers, Elizabeth Marie. / Revenue maximising adaptive auctioneer agent. Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings. 2008. pp. 340-347 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Auction theory has proven that auction revenue is influenced by factors such as the auction format and the auction parameters. The Revenue Maximising Adaptive Auctioneer (RMAA) agent model has been developed with the aim of generating maximum auction revenue by adapting the auction format and parameters to suit the auction environment. The RMAA agent uses a learning classifier system to learn which rules are profitable in a particular bidding environment. The profitable rules are then exploited by the RMAA agent to generate maximum revenue. The RMAA agent model can effectively adapt to a real time dynamic auction environment.",

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Pike, JC & Ehlers, EM 2008, Revenue maximising adaptive auctioneer agent. in Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5357 LNAI, pp. 340-347, 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Hanoi, Viet Nam, 15/12/08. https://doi.org/10.1007/978-3-540-89674-6_38

Revenue maximising adaptive auctioneer agent. / Pike, Janine Claire; Ehlers, Elizabeth Marie.
Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings. 2008. p. 340-347 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5357 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Pike JC, Ehlers EM. Revenue maximising adaptive auctioneer agent. In Intelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings. 2008. p. 340-347. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-89674-6_38

Revenue maximising adaptive auctioneer agent

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Keywords

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