Autonomous bidding agents : (Record no. 73102)

000 -LEADER
fixed length control field 04342nam a2200553 i 4500
001 - CONTROL NUMBER
control field 6267448
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712204708.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151223s2007 maua ob 001 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- print
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780262285957
-- electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic
082 04 - CLASSIFICATION NUMBER
Call Number 338.4/3
100 1# - AUTHOR NAME
Author Wellman, Michael P.,
245 10 - TITLE STATEMENT
Title Autonomous bidding agents :
Sub Title strategies and lessons from the trading agent competition /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (xi, 238 pages) :
490 1# - SERIES STATEMENT
Series statement Intelligent robotics and autonomous agents series
500 ## - GENERAL NOTE
Remark 1 "Multi-User"
500 ## - GENERAL NOTE
Remark 1 Academic Complete Subscription 2011-2012
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- The tac travel-shopping game -- Bidding in interdependent markets -- Price prediction -- Bidding with price predictions -- Machine learning and adaptivity -- Market-specific bidding strategies -- Experimental methods and strategic analysis -- Conclusion.
520 ## - SUMMARY, ETC.
Summary, etc E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry. The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents--to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types--encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding. Autonomous Bidding Agents provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors--who introduced TAC and created some of its most successful agents--offer both an overview of current research and new results. Michael P. Wellman is Professor of Computer Science and Engineering and member of the Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of Computer Science at Brown University. Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group at the University of Texas, Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.
700 1# - AUTHOR 2
Author 2 Stone, Peter,
700 1# - AUTHOR 2
Author 2 Greenwald, Amy.
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267448
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cambridge, Massachusetts :
-- MIT Press,
-- c2007.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [2007]
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
588 ## -
-- Description based on PDF viewed 12/23/2015.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electronic commerce.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Intelligent agents (Computer software)

No items available.