Download Autonomous Bidding Agents: Strategies and Lessons from the by Michael P. Wellman PDF

By Michael P. Wellman

ISBN-10: 026223260X

ISBN-13: 9780262232609

E-commerce more and more offers possibilities for self sustaining bidding brokers: laptop courses that bid in digital markets with out direct human intervention. computerized bidding ideas for an public sale of a unmarried sturdy with a identified valuation are quite basic; designing options for simultaneous auctions with interdependent valuations is a extra advanced venture. This publication provides algorithmic advances and process principles inside an built-in bidding agent structure that experience emerged from fresh paintings during this fast-growing zone of analysis in academia and undefined. The authors study a number of novel bidding ways that built from the buying and selling Agent festival (TAC), held each year due to the fact that 2000. The benchmark problem for competing agents--to purchase and promote a number of items with interdependent valuations in simultaneous auctions of other types--encourages rivals to use leading edge innovations to a typical job. The ebook strains the evolution of TAC and follows chosen brokers from notion via numerous competitions, proposing and studying specific algorithms built for independent bidding. independent Bidding brokers offers the 1st built-in remedy of equipment during this swiftly constructing area of AI. The authors--who brought TAC and created a few of its so much profitable agents--offer either an summary of present study and new effects. Michael P. Wellman is Professor of machine technology and Engineering and member of the unreal Intelligence Laboratory on the college of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of computing device technology at Brown college. Peter Stone is Assistant Professor of machine Sciences, Alfred P. Sloan learn Fellow, and Director of the educational brokers staff on the college of Texas, Austin. he's the recipient of the foreign Joint convention on synthetic Intelligence (IJCAI) 2007 pcs and concept Award.

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Extra resources for Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition

Sample text

Bars represent bids, with height proportional to offer price and varying shades encoding the respective bidding agents. 24 Chapter 2 The absence of skyrocketing hotel prices cleared the way for other strategic issues to come to the fore in TAC-01. , 2003b] between the approaches taken by the two agents finishing at the top of the standings in the TAC-01 finals, ATTac and livingagents. 4) on all available goods. ATTac’s priceprediction module uses machine-learning techniques (see Chapter 6) to generate distributions over hotel closing prices.

One of the top two TAC-01 agents, ATTac (this time with its price prediction module as originally trained from the 2001 data), returned to the top of the field in 2003. 3) above or within a few points of ATTac’s. It was becoming more difficult to distinguish one’s performance from the pack. With the perception of diminishing returns to strategic innovations, the community was already looking for new challenges. In 2003, one was readily provided by a new and exciting TAC market game in the domain of supply chain management (TAC/SCM) [Arunachalam and Sadeh, 2005].

Livingagents similarly makes a fixed decision about which entertainment to attempt to buy or sell, assuming they 6. For this estimate, livingagents used data from the preliminary rounds. As the designers note [Fritschi and Dorer, 2002], hotel prices in the finals turned out to be significantly lower than during the preliminary rounds, presumably because the more successful agents in the finals were better at keeping these prices down. Apparently, their performance did not suffer unduly from this difference.

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Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition by Michael P. Wellman


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