000			04342nam a2200553 i 4500
001			6267448
003			IEEE
005			20220712204708.0
006			m o d
007			cr |n|||||||||
008			151223s2007 maua ob 001 eng d
020			_z9780262232609 _qprint
020			_a9780262285957 _qelectronic
020			_z0262285959 _qelectronic
035			_a(CaBNVSL)mat06267448
035			_a(IDAMS)0b000064818b446c
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aHF5548.32 _b.W465 2007eb
082	0	4	_a338.4/3 _222
100	1		_aWellman, Michael P., _eauthor. _922864
245	1	0	_aAutonomous bidding agents : _bstrategies and lessons from the trading agent competition / _cMichael P. Wellman, Amy Greenwald, and Peter Stone.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2007.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2007]
300			_a1 PDF (xi, 238 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIntelligent robotics and autonomous agents series
500			_a"Multi-User"
500			_aAcademic Complete Subscription 2011-2012
504			_aIncludes bibliographical references (p. [227]-232) and indexes.
505	0		_aIntroduction -- 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.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aE-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.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aElectronic commerce. _95589
650		0	_aIntelligent agents (Computer software) _922865
655		0	_aElectronic books. _93294
700	1		_aStone, Peter, _d1971- _922866
700	1		_aGreenwald, Amy. _922867
710	2		_aIEEE Xplore (Online Service), _edistributor. _922868
710	2		_aMIT Press, _epublisher. _922869
776	0	8	_iPrint version _z9780262232609
830		0	_aIntelligent robotics and autonomous agents _921692
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267448
942			_cEBK
999			_c73102 _d73102