Download PDF by Sandip Sen (auth.), Longbing Cao, Ana L. C. Bazzan, Vladimir: Agents and Data Mining Interaction: 6th International

By Sandip Sen (auth.), Longbing Cao, Ana L. C. Bazzan, Vladimir Gorodetsky, Pericles A. Mitkas, Gerhard Weiss, Philip S. Yu (eds.)

ISBN-10: 3642154190

ISBN-13: 9783642154195

ISBN-10: 3642154204

ISBN-13: 9783642154201

This publication constitutes the refereed lawsuits of the sixth foreign Workshop on brokers and information Mining interplay, ADMI 2010, held in Toronto, Canada, in may perhaps 2010. The 15 revised complete papers provided have been conscientiously reviewed and chosen from 37 submissions. The papers are prepared in topical sections on brokers for information mining; information mining for brokers; info mining in brokers; and agent mining functions.

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Additional info for Agents and Data Mining Interaction: 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Toronto, ON, Canada, May 11, 2010, Revised Selected Papers

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Section 5 provides a discussion on the work that has been achieved and the issues that need to be addressed in the future. Concluding remarks are presented in section 6. 2 Background Due to multi-disciplinary interest in norms, several definitions for norms exist [4]. The definition of normative multi-agent systems as described by the researchers involved in the NorMAS 2007 workshop is as follows [7]. A normative multi-agent system is a multi-agent system organized by means of mechanisms to represent, communicate, distribute, detect, create, modify and enforce norms, and mechanisms to deliberate about norms and detect norm violation and fulfillment.

3 Overview of the Norm Identification Architecture In this section we provide an overview of the norm identification framework (called the norm engine) that we propose for an agent to infer norms in the agent society in which it is situated. The norm identification framework takes into account the social learning theory [15] that suggests that new behavior can be learnt through the observation of punishment and rewards. Figure 1 shows the architectural diagram of the norm identification framework.

G. [1]). These works assume that the agent somehow knows (a priori) what the norms of a society are. For example, an agent may have obtained the norm from a leader [2] or through an institution that prescribes what the norms of the society should be [3]. Only a few researchers have dealt with how an agent may infer what the norms of a newly joined society are [4]. Recognizing the norms of a society is beneficial to an agent. This process enables the agent to know what the normative expectation of a society is.

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Agents and Data Mining Interaction: 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Toronto, ON, Canada, May 11, 2010, Revised Selected Papers by Sandip Sen (auth.), Longbing Cao, Ana L. C. Bazzan, Vladimir Gorodetsky, Pericles A. Mitkas, Gerhard Weiss, Philip S. Yu (eds.)


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