Problem restructuring in negotiation
Management Science
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
A strategic decision model for multi-attribute bilateral negotiation with alternating
Proceedings of the 4th ACM conference on Electronic commerce
Using Similarity Criteria to Make Negotiation Trade-Offs
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Automated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Optimal Negotiation of Multiple Issues in Incomplete Information Settings
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Modeling complex multi-issue negotiations using utility graphs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Bilateral negotiation decisions with uncertain dynamic outside options
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optimal Agendas and Procedures for N-Issue Negotiation: An Inductive Definition
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Acquisition of a concession strategy in multi-issue negotiation
Web Intelligence and Agent Systems
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This paper presents a decentralized model that allows self-interested agents to reach "win-win" agreements in a multi-attribute negotiation. The model is based on an alternating-offer protocol. In each period, the proposing agent is allowed to make a limited number of offers. The responding agent can choose the best offer or reject all of them. In the case of rejection, agents exchange their roles and the negotiation proceeds to the next period. To make counteroffers, an agent first uses the heuristic of choosing, on an indifference curve (or surface), the offer that is closest to the best offer made by the opponent in the previous period, and then taking this offer as the seed, chooses several other offers randomly in a specified neighborhood of this seed offer. Experimental results show that this model can make agents reach near Pareto optimal agreements in general situations where agents have complex preferences on the attributes and incomplete information. Moreover, different from other solutions for multi-attribute negotiations, this model does not require the presence of a mediator.