This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the area of automated negotiation. It gives new practical and theoretical insights into the design and evaluation of automated negotiators. It describes an innovative negotiating agent framework that enables systematic exploration of the space of possible negotiation strategies by recombining different agent components. Using this framework, new and effective ways are formulated for an agent to learn, bid, and accept during a negotiation. The findings have been evaluated in four annual instantiations of the International Automated Negotiating Agents Competition (ANAC), the results of which are also outlined here. The book also describes several methodologies for evaluating and comparing negotiation strategies and components, with a special emphasis on performance and accuracy measures.
表中的内容
Introduction.- Background.- A Component-based Architecture to Explore the Space of Negotiation Strategies.- Effective Acceptance Conditions.- Accepting Optimally with Incomplete Information.- Measuring the Performance of Online Opponent Models.- Predicting the Performance of Opponent Models.- A Quantitative Concession-Based Classification Method of Bidding Strategies.- Optimal Non-adaptive Concession Strategies.- Putting the Pieces Together.- Conclusion.
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