Track: Artificial Intelligence

Abstract

Real-world negotiation problems are often complex, since they require evaluation and decision-making based on multiple issues of qualitative and quantitative nature, involving temporal constraints and dealing with decentralized relevant information. In such environments with incomplete information, mediated-negotiations have been proved to be faster and more efficient than non-mediated ones. The aim of any mediation scenario is to help the negotiators reach a mutually beneficial pareto-optimal agreement which is better to all agents than opting out the negotiations. In this paper, we propose to use the Analytic Hierarchy Process (AHP) to enhance consensus in bilateral multi-issue negotiation where one agent has no knowledge about the preferences of the other agents. This work focuses on mediated negotiation, where two agents try to reach an agreement over a range of qualitative and quantitative issues. We assume that the mediator agent adopts the AHP method to construct the ranking of the alternatives based on both agents’ preferences defined beforehand. Based on a case study, we show how AHP, used as a qualitative and quantitative evaluation method, can provide a simple and effective decision making leading to an efficient and timely conflict resolution.