Towards genetically optimised responsive negotiation agents

Lau, Raymond Y. K., Tang, Maolin, & Wong, On (2004) Towards genetically optimised responsive negotiation agents. In Zhong, Ning, Bradshaw, Jeffrey, Pal, Sankar K., Talia, Domenico, Liu, Jiming, & Cercone, Nick (Eds.) IEEE/WIC/ACM International Conference on Intelligent Agent Technology, September 2004, Beijing.

View at publisher


Real-world negotiations are characterised by combinatorially complex negotiation spaces, tough deadlines, bounded agent rationality, very limited information about the opponents, and volatile negotiator preferences. Classical negotiation models fail to address most of these issues. This paper illustrates our practical negotiation agents which are empowered by an effective and efficient genetic algorithm to deal with complex, incomplete, and dynamic negotiation spaces arising in real-world applications. Initial experiment demonstrates that our genetically optimised adaptive negotiation agents outperform a theoretically optimal negotiation model when time pressure exists. Our research work opens the door to the development of responsive and adaptive negotiation agents for real-world applications.

Impact and interest:

20 citations in Scopus
12 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

145 since deposited on 17 Jul 2007
6 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 8673
Item Type: Conference Paper
Refereed: Yes
Keywords: genetic algorithm, negotiation, e, commerce
DOI: 10.1109/IAT.2004.1342958
ISBN: 0769521010
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2004 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 17 Jul 2007 00:00
Last Modified: 29 Feb 2012 13:09

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page