By Andrew Byde (auth.), Julian Padget, Onn Shehory, David Parkes, Norman Sadeh, William E. Walsh (eds.)
This booklet constitutes the completely refereed post-proceedings of the 4th foreign Workshop on Agent-Mediated digital trade, AMEC 2002, held in Bologna, Italy in July 2002 through the AAMAS 2002 conference.
The 20 revised complete papers provided including an introductory survey by way of the amount editors have been rigorously chosen and more desirable in the course of rounds of reviewing and revision. The publication supplies a distinct assessment of the cutting-edge of designing mechanisms and structures for agent-mediated e-commerce- The papers are equipped in topical sections on digital auctions, negotiations, and digital markets.
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Additional 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
Iterative Deepening A* (IDA*) is a variant of A* that uses the same two functions g and h in a depth- rst search which keeps in memory only the current path from the root to a particular node. In each 1 Lowest for a minimization problem, highest for a maximization problem. Bidtree Ordering in IDA* Combinatorial Auction Winner-Determination 21 iteration of IDA*, search depth is limited by a threshold value on the evaluation function f . At the rst iteration, this threshold is typically the estimate of the cost of the initial node.
Gini Because the winner-determination problem must be solved in the context of a real-time negotiation scenario, and because the customer agent s RFQ must inform potential bidders of the earliest time they may expire their bids, we must be concerned with both performance and predictability of the winnerdetermination process. The winner determination problem can be approached in various ways: dynamic programming , integer programming [1,3], approximate solutions , and search methods [16,17,9].
15. Michael H. Rothkopf, Alexander Peke˘c, and Ronald M. Harstad. Computationally manageable combinatorial auctions. Management Science, 44(8):1131–1147, 1998. 16. Tuomas Sandholm. Algorithm for optimal winner determination in combinatorial auctions. Artiﬁcial Intelligence, 135:1–54, 2002. 17. Tuomas Sandholm, Subhash Suri, Andrew Gilpin, and David Levine. CABOB: A fast optimal algorithm for combinatorial auctions. In Proc. of the 17th Joint Conf. on Artiﬁcial Intelligence, Seattle, WA, USA, August 2001.
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.)