Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Learning Negotiation Skills: Four Models of Knowledge Creation and Transfer
Management Science
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
From problems to protocols: Towards a negotiation handbook
Decision Support Systems
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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 exploits a technique to learn a model of opponent preferences in a single negotiation session. An opponent model may be used to achieve at least two important goals in negotiation. First, it can be used to recognize, avoid and respond appropriately to exploitation, which differentiates the strategy proposed from commonly used concession-based strategies. Second, it can be used to increase the efficiency of a negotiated agreement by searching for Pareto-optimal bids. A negotiation strategy should be efficient, transparent, maximize the chance of an agreement and should avoid exploitation. We argue that the proposed strategy satisfies these criteria and analyze its performance experimentally.