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 suggestions to a wide and various array of difficulties, bearing on person agent behaviors, interplay, and collective habit. a wide selection of digital trade situations and platforms, together with agent methods to those, were studied lately. those reviews recommend versions that help the - signal and the research at either the extent of the only 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 foreign Joint convention on independent 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 synthetic Intelligence (AAAI 2008). the first, and complementary, target of either workshops was once to proceed to carry - gether novel paintings from assorted fields that concentrate on modeling, implementation, and review of computational buying and selling associations and/or agent strategies.

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Additional info for Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis: AAMAS Workshop, AMEC 2008, Estoril, Portugal, May 12-16, 2008, and AAAI Workshop, TADA 2008, Chicago, IL, USA, July 14, 2008, Revised Selected Papers

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In this case, the consumer’s next requests become far away from the best offer that can be proposed by the producer. Therefore, it takes more time to reach the best offer. Table 2. 8 3 6 13 6 26 R. Aydo˘gan, N. Tas¸demir, and P. Yolum Table 3. 8 The second pattern that we investigated in our experiments involves same numbers of services of moderately desired level and highly desired level. For first preference profile, the stock has five services of depth eight, five services of depth nine , five services of depth 16 and five services of depth 17.

4 Producer’s Strategy for Counter Offer Unless the producer does not own the requested services, it has a tendency to offer a service from its stock, which is most similar to the last request of the consumer. To estimate the similarity, Tversky’s similarity measure [6] comparing two vectors in terms of the number of exactly matching features is used. The formula for this similarity metric is shown in Equation (1) [6]. Here, common represents the number of matched attributes whereas dif f erent represents the number of the different attributes.

For instance, assume that the producer offers a service of depth three. From the consumer point of view, it does not make sense to ask for a service whose depth is less than three. Reasoning and Negotiating with Complex Preferences Using CP-Nets 23 Algorithm 1. at(value) In the given algorithm, upper represents the upper depth and lower represents the lower depth (initially zero). The consumer first asks for any service whose depth is equal to curDepth = (upper + lower)/2. According to the algorithm, firstly whether there exists any service node that was not be used previously at the curDepth is checked (Lines 1-5).

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