@inbook{2e042cd8298241a4a909ccc32abd1c5c,
title = "Negotiating with unknown opponents: Toward multi-lateral agreement in real-time domains",
abstract = "Automated negotiation has been gained a mass of attention mainly because of its broad application potential in many fields. This work studies a prominent class of automated negotiations – multi-lateral multi-issue negotiations under real-time constraints, where the negotiation agents are given no prior information about their opponents{\textquoteright} preferences over the negotiation outcome space. A novel negotiation approach is proposed that enables an agent to obtain efficient agreements in this challenging multi-lateral negotiations. The proposed approach achieves that goal by, (1) employing sparse pseudo-input Gaussian processes (SPGPs) to model 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.",
author = "Siqi Chen and J. Hao and Shuang Zhou and Gerhard Weiss",
year = "2017",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "219--229",
editor = "K. Fuijta",
booktitle = "Modern Approaches to Agent-based Complex Automated Negotiation",
address = "Germany",
}