Revenue maximising adaptive auctioneer agent

Janine Claire Pike, Elizabeth Marie Ehlers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationIntelligent Agents and Multi-Agent Systems - 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008, Proceedings
Number of pages8
Publication statusPublished - 2008
Event11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008 - Hanoi, Viet Nam
Duration: 15 Dec 200816 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5357 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008
Country/TerritoryViet Nam


  • Agent auctions
  • Auction theory
  • Learning classifier system
  • Reinforcement learning
  • ZCS

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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