Toward Efficient Agreements in Real-Time Multilateral Agent-Based Negotiations

Siqi Chen*, Jianye Hao, Gerhard Weiss, Shuang Zhou, Zili Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

3 Citations (Web of Science)


Negotiations among autonomous agents have gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences over the space of negotiation outcomes. We propose a novel negotiation approach which enables an agent to reach an efficient agreement with multiple opponents. The proposed approach achieves that goal by, 1) employing sparse pseudo-input Gaussian processes to model the behavior of opponents, 2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and 3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation. The experimental results show, both from the standard mean-score perspective and the perspective of empirical game theory, that the agent applying the proposed approach outperforms the state-of-the-art negotiation agents from the recent Automated Negotiating Agents Competition (ANAC) in a variety of negotiation domains.
Original languageEnglish
Title of host publicationProceedings 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)
Publication statusPublished - 2015
Event27th IEEE International Conference on Tools with Artificial Intelligence - Vietri sul Mare, Italy
Duration: 9 Nov 201511 Nov 2015


Conference27th IEEE International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2015
CityVietri sul Mare

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