ATTac-2001: A Learning, Autonomous Bidding Agent

Stone, Peter; Schapire, Robert E.; Csirik, János; Littman, Michael L.; McAllester, David

doi:10.1007/3-540-36378-5_9

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…Lucking-Reiley et al (2000) presented an exploratory analysis of the determinants of prices in online auctions for collectable one-cent coins at the eBay Web site. ATTac-2001(Stone, Schapire, Csirik, Littman, & McAlleste, 2001 applies boosting techniques to learn conditional distributions of auction clearing prices. The core of ATTac-2001 is to learn a model of the empirical price dynamics based on past data, and utilize the model to compute the greatest extent possible optimal bids analytically.…”

Section: Price Prediction On Online Marketmentioning

confidence: 99%

Intelligent spider for information retrieval to support mining-based price prediction for online auctioning

Chan¹

2008

Expert Systems with Applications

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Section: Price Prediction On Online Marketmentioning

confidence: 99%

Intelligent spider for information retrieval to support mining-based price prediction for online auctioning

Chan¹

2008

Expert Systems with Applications

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“…Much recent literature on bidding agent design follows the decision-theoretic approach, e.g. (Boutilier, Goldszmidt, & Sabata, 1999;Byde, 2002;Greenwald & Boyan, 2004;Stone et al, 2002;Mackie-Mason et al, 2004;Osepayshvili et al, 2005).…”

Section: Game-theoretic and Decision-theoretic Approachesmentioning

confidence: 99%

Bidding agents for online auctions with hidden bids

Jiang

Leyton‐Brown

2006

Mach Learn

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There is much active research into the design of automated bidding agents, particularly for environments that involve multiple decoupled auctions. These settings are complex partly because an agent's strategy depends on information about other bidders' interests. When bidders' valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders' valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these "hidden bids" can introduce bias into the estimation of valuation distributions. To overcome this problem, we propose an EM-based algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders' valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques.

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“…It is the basis for the ongoing Trading Agent Competition [36], which has stimulated interesting research on bidding behavior in autonomous agents, such as [37].…”

Section: Multi-agent Negotiationmentioning

confidence: 99%

MAGNET: A Multi-Agent System using Auctions with Temporal and Precedence Constraints

Collins

Gini²

2006

Multiagent Based Supply Chain Management

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Abstract. We consider the problem of rational, self-interested, economic agents who must negotiate with each other in order to carry out their plans. Customer agents express their plans in the form of task networks with temporal and precedence constraints. The market runs a combinatorial reverse auction, in which supplier agents submit bids specifying prices for combinations of tasks, along with time windows and duration data that the customer may use to compose a work schedule. The presence of temporal and precedence constraints among the items at auction requires extensions to the standard winner-determination procedures for combinatorial auctions, and the use of the enhanced winnerdetermination procedure within the context of a real-time negotiation requires that we predict its runtime when planning the negotiation process. We address two specific issues related to this problem. The first is the need for a market infrastructure to support decision processes. We propose a set of requirements for a market that can support this type of negotiation, and describe an architecture that can meet these requirements. We also describe the high-level design of an agent that can act as a customer in this environment, and discuss the decision behaviors such an agent must implement to maximize its utility. The second issue we consider is the determination of auction winners. We explore and characterize a winner determination method, which is an extension of the bidtree-based Iterative-Deepening A* (IDA*) formulation proposed by Sandholm.

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ATTac-2001: A Learning, Autonomous Bidding Agent

Cited by 12 publications

References 20 publications

Intelligent spider for information retrieval to support mining-based price prediction for online auctioning

Intelligent spider for information retrieval to support mining-based price prediction for online auctioning

Bidding agents for online auctions with hidden bids

MAGNET: A Multi-Agent System using Auctions with Temporal and Precedence Constraints

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