Abstract
In multi-agent systems, a multilateral closed negotiation, where the opponent’s strategy and utility are closed, is an important class of automated negotiations. However, most existing negotiation protocols haven’t addressed the private information revealed by agents. During negotiations, such private information as agents preferences must be revealed fairly because each agent loses utility in them. In this paper, we propose a negotiation protocol that addresses the fairness of revealing each agent’s private information. First, we propose a new measure of revealing each agent’s private information, which is based on the accuracy of estimating opponents’ utility functions. Next, the negotiation protocol adjusts the number of offers by each agent based on a new measure. This adjustment encourages agents who reveal less private information than other agents to reveal more offers. In the experiments, we compared and investigated the fairness of revealing private information by tournaments among state-of-the-art agents in ANAC2016 using our proposed negotiation protocol. The experimental results demonstrate that our proposed negotiation protocol with the adjustment improves the fairness of the revealed private information and a trade-off between the revealed private information and individual utility exists.
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This work was supported by CREST, JST, and JSPS KAKENHI Grant Numbers 15H01703 and 26730116.
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Shinohara, H., Fujita, K. (2017). Alternating Offers Protocol Considering Fair Privacy for Multilateral Closed Negotiation. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds) PRIMA 2017: Principles and Practice of Multi-Agent Systems. PRIMA 2017. Lecture Notes in Computer Science(), vol 10621. Springer, Cham. https://doi.org/10.1007/978-3-319-69131-2_36
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DOI: https://doi.org/10.1007/978-3-319-69131-2_36
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