Designing a successful adaptive agent for TAC Ad auction

Meng Chang; Minghua He; Xudong Luo

doi:10.3233/978-1-60750-606-5-587

Designing a successful adaptive agent for TAC Ad auction

Meng Chang, Minghua He, Xudong Luo

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper describes the design and evaluation of Aston-TAC, the runner-up in the Ad Auction Game of 2009 International Trading Agent Competition. In particular, we focus on how Aston-TAC generates adaptive bid prices according to the Market-based Value Per Click and how it selects a set of keyword queries to bid on to maximise the expected profit under limited conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments.

Original language	English
Title of host publication	Frontiers in artificial intelligence and applications
Subtitle of host publication	ECAI 2010 - 19th European conference on artificial intelligence
Editors	Helder Coelho, Rudi Studer, Michael Wooldridge
Publisher	IOS
Pages	587-592
Number of pages	6
Volume	215
ISBN (Print)	978-1-60750-605-8
DOIs	https://doi.org/10.3233/978-1-60750-606-5-587
Publication status	Published - 2010
Event	19th European Conference on Artificial Intelligence - Lisbon, Portugal Duration: 16 Aug 2010 → 20 Aug 2010 http://ecai2010.appia.pt/

Conference

Conference	19th European Conference on Artificial Intelligence
Abbreviated title	ECAI 2010
Country/Territory	Portugal
City	Lisbon
Period	16/08/10 → 20/08/10
Internet address	http://ecai2010.appia.pt/

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10.3233/978-1-60750-606-5-587

Designing a Successful Adaptive Agent for TAC Ad AuctionFinal published version, 444 KB

http://www.booksonline.iospress.nl/Content/View.aspx?piid=17816

Cite this

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title = "Designing a successful adaptive agent for TAC Ad auction",

abstract = "This paper describes the design and evaluation of Aston-TAC, the runner-up in the Ad Auction Game of 2009 International Trading Agent Competition. In particular, we focus on how Aston-TAC generates adaptive bid prices according to the Market-based Value Per Click and how it selects a set of keyword queries to bid on to maximise the expected profit under limited conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments.",

author = "Meng Chang and Minghua He and Xudong Luo",

year = "2010",

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language = "English",

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editor = "Helder Coelho and Rudi Studer and Michael Wooldridge",

booktitle = "Frontiers in artificial intelligence and applications",

publisher = "IOS",

address = "Netherlands",

note = "19th European Conference on Artificial Intelligence, ECAI 2010 ; Conference date: 16-08-2010 Through 20-08-2010",

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Chang, M, He, M & Luo, X 2010, Designing a successful adaptive agent for TAC Ad auction. in H Coelho, R Studer & M Wooldridge (eds), Frontiers in artificial intelligence and applications: ECAI 2010 - 19th European conference on artificial intelligence. vol. 215, IOS, pp. 587-592, 19th European Conference on Artificial Intelligence, Lisbon, Portugal, 16/08/10. https://doi.org/10.3233/978-1-60750-606-5-587

Designing a successful adaptive agent for TAC Ad auction. / Chang, Meng; He, Minghua; Luo, Xudong.
Frontiers in artificial intelligence and applications: ECAI 2010 - 19th European conference on artificial intelligence. ed. / Helder Coelho; Rudi Studer; Michael Wooldridge. Vol. 215 IOS, 2010. p. 587-592.

Research output: Chapter in Book/Published conference output › Conference publication

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AB - This paper describes the design and evaluation of Aston-TAC, the runner-up in the Ad Auction Game of 2009 International Trading Agent Competition. In particular, we focus on how Aston-TAC generates adaptive bid prices according to the Market-based Value Per Click and how it selects a set of keyword queries to bid on to maximise the expected profit under limited conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments.

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Designing a successful adaptive agent for TAC Ad auction

Abstract

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