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Exploring the strategy space of negotiating agents = a framework for bidding, learning and accepting in automated negotiation /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Exploring the strategy space of negotiating agents/ by Tim Baarslag.
Reminder of title:	a framework for bidding, learning and accepting in automated negotiation /
Author:	Baarslag, Tim.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xxi, 276 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Intelligent agents (Computer software) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-28243-5
ISBN:	9783319282435

Exploring the strategy space of negotiating agents = a framework for bidding, learning and accepting in automated negotiation /
Baarslag, Tim.

Exploring the strategy space of negotiating agentsa framework for bidding, learning and accepting in automated negotiation /[electronic resource] :by Tim Baarslag. - Cham :Springer International Publishing :2016. - xxi, 276 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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.

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.

ISBN: 9783319282435

Standard No.: 10.1007/978-3-319-28243-5doiSubjects--Topical Terms:

558701
Intelligent agents (Computer software)

LC Class. No.: QA76.76.I58

Dewey Class. No.: 006.3

Exploring the strategy space of negotiating agents = a framework for bidding, learning and accepting in automated negotiation /
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505 0 $a 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.
520 $a 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.
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