Abstract
Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important goals in negotiation. It may be used to increase the efficiency of negotiation by searching for Pareto optimal bids and to avoid exploitation by making moves that mirror the move of the other party. The performance of the proposed negotiation strategy is analyzed in a tournament setup.
Original language | English |
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Title of host publication | 8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1238-1239 |
Number of pages | 2 |
Volume | 2 |
ISBN (Print) | 9781615673346 |
Publication status | Published - 1 Jan 2009 |
Externally published | Yes |
Event | 8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 - Budapest, Hungary Duration: 10 May 2009 → 15 May 2009 |
Conference
Conference | 8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 |
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Country/Territory | Hungary |
City | Budapest |
Period | 10/05/09 → 15/05/09 |
Keywords
- Automated multi-issue negotiation
- Bayesian learning
- Negotiation strategy
- Opponent modelling
- Tit-for-tat