Agent-Mediated Electronic Commerce and Trading Agent Design by Mathijs de Weerdt, Yingqian Zhang (auth.), Wolfgang Ketter,

By Mathijs de Weerdt, Yingqian Zhang (auth.), Wolfgang Ketter, Han La Poutré, Norman Sadeh, Onn Shehory, William Walsh (eds.)

The layout and research of digital trade structures within which brokers are - ployed includes discovering options to a wide and numerous array of difficulties, pertaining to person agent behaviors, interplay, and collective habit. a large choice of digital trade situations and platforms, together with agent techniques to those, were studied lately. those experiences recommend types that aid the - signal and the research at either the extent of the one agent and the extent of the multiagent process. th This quantity comprises revised, chosen papers from the ten Workshop on Agent- Mediated digital trade (AMEC-X), co-located with the seventh overseas Joint convention on self sustaining brokers and Multiagent platforms (AAMAS 2008), th and from the 6 Workshop on buying and selling Agent layout and research (TADA), - positioned with the twenty third AAAI convention on man made Intelligence (AAAI 2008). the first, and complementary, target of either workshops used to be to proceed to deliver - gether novel paintings from varied fields that target modeling, implementation, and review of computational buying and selling associations and/or agent strategies.

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Robu, and H. La Poutr´e Let G be the set of n goods for sale in a temporal sequence of auctions and vsyn (Gsub ) be the valuation the synergy bidder has for Gsub G. Then assume that vsyn (G) > 0 and ∀Gsub G, vsyn (Gsub ) = 0. In other words, the synergy bidder only desires a bundle of all the goods considered in the model. Gn ∈ G are sold individually through sequential, first-price, sealedbid auctions. Here we choose the auctions to be first price, as they are more tractable to study using game-theoretic analysis.

To be fair, we use five different service stocks in this pattern for the first customer profile: Stock1A . . Stock5A. The second preference profile is composed of 11 services of depth 12, eight services of depth 13 and one service of depth 20. Similarly, we use five different stocks in this pattern for the second customer profile: Stock1B . . Stock5B. Different from other strategies, upper random strategy is run at five times and the average value is used for the evaluation. Table 2 and Table 3 show the number of interactions with the producer for each strategy to obtain the producer’s best offer for the first preference profile and second preference profile respectively.

40 L. Mous, V. Robu, and H. 1 Synergy Bidder’s Strategy Let Q(zA , zB , X, It , bt ) be the expected profit of the synergy bidder when bidding bt at each time step t. e. items it already acquired) at time t. The profit-maximizing bid b∗t is computed as: b∗t = argmaxbt Q(zA , zB , X, It , bt ) (8) This value can be computed through standard dynamic programming. Exact search for b∗t is computationally feasible for the experiments reported in this paper (as the size of the desired bundle and the length of the auction sequence are limited).

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