Titlebook: Recent Advances in Agent-based Complex Automated Negotiation; Naoki Fukuta,Takayuki Ito,Valentin Robu Book 2016 Springer International Pub

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Prediction of the Opponent’s Preference in Bilateral Multi-issue Negotiation Through Bayesian Learni issues. Besides, a counter-offer proposition algorithm is integrated in our approach to help agents to generate mutual beneficial offers based on the preference learning result. Also, the experimental results indicate the good performance of the proposed approach in aspects of utility gain and negotiation efficiency.

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Optimal Non-adaptive Concession Strategies with Incomplete Information outperform current state of the art approaches in terms of obtained utility. Our results open the way for a new and general concession strategy that can be combined with various existing learning and accepting techniques to yield a fully-fledged negotiation strategy for the alternating offers setting.

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Using Transfer Learning to Model Unknown Opponents in Automated Negotiationsious negotiation tasks and the model learnt from the current negotiation session. Extensive experimental results show the applicability and effectiveness of both approaches. Moreover, the robustness of the proposed approaches is evaluated using empirical game theoretic analysis.

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1860-949X sed Complex Automated Negotiations (ACAN) ACAN‘14 held in Pa.This bookcovers recent advances in Complex Automated Negotiations as a widely studiedemerging area in the field of Autonomous Agents and Multi-Agent Systems. The book includes selectedrevised and extended papers from the 7th International

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small number of exceptions, ergodic theory dealt primarily with averaging problems and general qualitative questions, while now it is a powerful amalgam of methods used for the analysis of statistical properties of dyna­ mical systems. For this reason, the problems of ergodic theory now interest no

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