Phoenix: A Threshold Function Based Negotiation Strategy Using Gaussian Process Regression and Distance-Based Pareto Frontier Approximation
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AbstractAutomated negotiation is of great interest in artificial intelligence. An effective automated negotiation strategy can facilitate human in reaching better negotiation outcomes benefiting from the adoption of advanced computational methods. This paper deals with multi-lateral multi-issue negotiation where opponents’ preferences and strategies are unknown. A novel negotiation strategy called Phoenix is proposed following the negotiation setting adopted in The Sixth International Automated Negotiating Agents Competition (ANAC 2015) [13]. In attempt to maximize individual utility and social welfare, we propose two highlighted methods – Gaussian Process Regression (GPR) and Distance-based Pareto Frontier Approximation (DPFA). Integrating the idea of these methods into a single function called threshold function, we show that Phoenix is a fully adaptive, cooperative and rationally designed strategy.
All Author(s) ListMax W. Y. Lam , Ho-fung Leung
All Editor(s) ListKatsuhide Fujita, Quan Bai, Takayuki Ito, Minjie Zhang, Fenghui Ren, Reyhan Aydoğan, Rafik Hadfi
EditionPart II
Book titleModern Approaches to Agent-based Complex Automated Negotiation
Series TitleStudies in Computational Intelligence
Number in Series674
PublisherSpringer International Publishing
Pages201 - 212
LanguagesEnglish-United States

Last updated on 2020-06-08 at 03:52