Theory for coordinating concurrent hierarchical planning agents using summary information
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
A layered approach to complex negotiations
Web Intelligence and Agent Systems
International Journal of Human-Computer Studies
Algorithmic Game Theory
Effective bidding and deal identification for negotiations in highly nonlinear scenarios
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Reasoning about multi-attribute preferences
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A hierarchical protocol for coordinating multiagent behaviors
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
A D-S theory based AHP decision making approach with ambiguous evaluations of multiple criteria
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Negotiation is usually dealt with package deals, where all issues are considered together. We propose a hierarchical negotiation approach where various sets of issues are negotiated at different levels. As the number of interdependent issues in negotiation grows, it becomes computationally intractable to generate proposals on all issues and to evaluate such proposals. By modeling all issues in negotiation as a hierarchical structure, it significantly reduces the search space in each negotiation session and improves the probability of finding better negotiation outcomes. Since a numeric utility function for multiple interdependent issues is hard to acquire in most real-world applications, a constraint-based preference representation is adopted in this approach to mimic the preference model used by human. This paper presents the design of this hierarchical negotiation model, and illustrates how it works using an example of vehicle design process. Experimental results are presented to evaluate the the performance and efficiency of this approach.