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Full Description
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.
Contents
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.
