New PDF release: Agent-Mediated Electronic Commerce IV. Designing Mechanisms

By Andrew Byde (auth.), Julian Padget, Onn Shehory, David Parkes, Norman Sadeh, William E. Walsh (eds.)

ISBN-10: 3540003274

ISBN-13: 9783540003274

ISBN-10: 3540363785

ISBN-13: 9783540363781

This booklet constitutes the completely refereed post-proceedings of the 4th overseas Workshop on Agent-Mediated digital trade, AMEC 2002, held in Bologna, Italy in July 2002 in the course of the AAMAS 2002 conference.

The 20 revised complete papers awarded including an introductory survey by means of the amount editors have been rigorously chosen and enhanced in the course of rounds of reviewing and revision. The publication provides a distinct evaluate of the cutting-edge of designing mechanisms and platforms for agent-mediated e-commerce- The papers are prepared in topical sections on digital auctions, negotiations, and digital markets.

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Extra info for Agent-Mediated Electronic Commerce IV. Designing Mechanisms and Systems: AAMAS 2002 Workshop on Agent-Mediated Electronic Commerce Bologna, Italy, July 16, 2002 Revised Papers

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It can be approached via dynamic programming [15] for small sets of bids. With certain restrictions on bids, Nisan [12] expresses the winner determination problem as a standard mixed integer programming problem, and formalizes several bidding languages. Andersson [1] also proposes an integer programming method showing that the integer programming enables the management of very general problems by the use of standard algorithms and commercially available software such as CPLEX. In the works of Rassenti [14] and Fujishima [7] the winner determination in combinatorial auctions is attempted via approximation algorithms.

30 J. Collins, G. Demir, and M. 1 0 1e+00 1e+01 1e+02 1e+03 1e+04 1e+05 IDA* Search Time (msec) 1e+06 1e+07 Fig. 9. IDA* run-time distributions for problems with 30 tasks, bid count ranging from 80 bids to 265 bids. 069 x) 1e+00 80 100 120 140 160 180 200 220 240 260 280 Bid Count Fig. 10. 95th , 50th and 5th percentile of runtime distribution from Figure 9, and regression curve that minimizes log square deviation. The goal of this type of analysis is to nd empirical relationships between search e ort and a variety of problem-size metrics that can be measured or estimated prior to submitting an RFQ.

4. Example bidtree sorted by increasing bid count (top) and by decreasing bid count (bottom). Bidtree Ordering in IDA* Combinatorial Auction Winner-Determination 25 The real di erence between the two bidtrees in Figure 4 is the size of the complementary partitions in the nodes that are generated using them. This is easy to see with respect to the root node, where the set of candidate bids is the entire left subtree, and the sizes of the complementary partitions for the rst tier of children is at most the size of the right subtree.

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Agent-Mediated Electronic Commerce IV. Designing Mechanisms and Systems: AAMAS 2002 Workshop on Agent-Mediated Electronic Commerce Bologna, Italy, July 16, 2002 Revised Papers by Andrew Byde (auth.), Julian Padget, Onn Shehory, David Parkes, Norman Sadeh, William E. Walsh (eds.)


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