Multiagent negotiation on multiple issues with incomplete information: extended abstract

Authors:
Ronghuo Zheng;Nilanjan Chakraborty;Tinglong Dai;Katia Sycara
Affiliations:
Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Year:
2013

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Abstract

We present a reactive offer generation method for general multi-agent multi-attribute negotiation, where the agents have non-linear utility functions and no information about the utility functions of other agents. We prove the convergence of the proposing method and characterize the convergence rate under a finite negotiation time. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the solution obtained from our protocol is quite close to the Nash bargaining solution.