New PDF release: Agent-Mediated Electronic Commerce. Designing Trading

By Sofia Ceppi, Nicola Gatti (auth.), Esther David, Enrico Gerding, David Sarne, Onn Shehory (eds.)

ISBN-10: 3642151167

ISBN-13: 9783642151163

ISBN-10: 3642151175

ISBN-13: 9783642151170

This quantity comprises 18 completely refereed and revised papers detailing contemporary advances in examine on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially awarded on the eleventh overseas Workshop on Agent-Mediated digital trade (AMEC 2009) collocated with AAMAS 2009 in Budapest, Hungary, or the 2009 Workshop on buying and selling Agent layout and research (TADA 2009) collocated with IJCAI 2009 in Pasadena, CA, united states. The papers concentrate on subject matters similar to person agent habit and agent interplay, collective habit, mechanism layout, and computational elements, all within the context of e-commerce functions like buying and selling, auctions, or negotiations. They mix techniques from varied fields of arithmetic, computing device technological know-how, and economics resembling synthetic intelligence, disbursed structures, operations learn, and video game idea.

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Read or Download Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AAMAS Workshop, AMEC 2009, Budapest, Hungary, May 12, 2009, and IJCAI Workshop, TADA 2009, Pasadena, CA, USA, July 13, 2009, Selected and Revised Paper PDF

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Extra resources for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AAMAS Workshop, AMEC 2009, Budapest, Hungary, May 12, 2009, and IJCAI Workshop, TADA 2009, Pasadena, CA, USA, July 13, 2009, Selected and Revised Paper

Sample text

Scoring rules have been used in related work to elicit information and induce effort, for example in [6,9]. Our work differs from this and related approaches, however. First, in our case experts need to make observations about multiple service providers, but in the end only one of these providers is selected. This means that, if the scoring rule is conditioned on the outcome, only the information about the selected provider can be evaluated. The experts may therefore misreport their information about other providers.

The transfer, or reward, for an agent who provides no new information is zero. In order to prove various properties that arise when using the marginal-contribution rule as a transfer function, we need to carefully define the utility of an agent. In particular, we need to specify an agent’s beliefs about the observations made by other agents since the transfer that an agent i receives depends both on the service provider chosen and on the total number of observations made by other agents. If there are few observations, then the observations of agent i may be more informative and thus result in a higher transfer.

That is, C can always decide to not use a service provider (and thus get value 0 for not completing the task). While C has no information about the reliability of the service providers, we assume that it can ask for information from a group of experts, N = {e1 , . . , en }. Each agent ei ∈ N has some experience with each service provider. In particular, we assume that expert ei has interacted with provider j in the past, and thus has observed ai (j) successes and bi (j) failures, where these successes and failures are drawn from the true underlying distribution of the service provider.

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Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AAMAS Workshop, AMEC 2009, Budapest, Hungary, May 12, 2009, and IJCAI Workshop, TADA 2009, Pasadena, CA, USA, July 13, 2009, Selected and Revised Paper by Sofia Ceppi, Nicola Gatti (auth.), Esther David, Enrico Gerding, David Sarne, Onn Shehory (eds.)


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