Abstract
Multilateral negotiation and its importance in today’s society, makes it an interesting application in the Artificial Intelligence domain. This paper proposes a new generic framework for multilateral multi-issue Argumentation-Based Negotiation. Agents are first clustered based on their offers to reduce the negotiation complexity. These agents negotiate via a mediator who has two roles: (1) organize the dialogue between them and (2) step in when no mutual agreement is found to get them out of the bottleneck, we formulate this problem as a linear problem where the mediator maximizes both parties’ utilities fairly. To highlight the performance of our framework, we tested it on the tourism domain using real data. We study the cases where agents are: not clustered, clustered using hard clustering techniques and clustered using soft clustering techniques. We also study the impact of the mediator in each variant by detecting agreements reached with and without his help.
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Bouslama, R., Ayachi, R., Ben Amor, N. (2019). A New Generic Framework for Mediated Multilateral Argumentation-Based Negotiation Using Case-Based Reasoning. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_2
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