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

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

This e-book constitutes the refereed complaints of the sixth overseas Workshop on brokers and knowledge Mining interplay, ADMI 2010, held in Toronto, Canada, in may possibly 2010. The 15 revised complete papers offered have been conscientiously reviewed and chosen from 37 submissions. The papers are prepared in topical sections on brokers for information mining; facts mining for brokers; information mining in brokers; and agent mining functions.

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Extra 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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ADMI 2010, LNCS 5980, pp. 43–58, 2010. R. Savarimuthu et al. architecture is based on observation of interactions between agents. It enables an autonomous agent to identify the obligation norms in a society using the Obligation Norm Inference (ONI) algorithm presented here. Using an auction example, we demonstrate how an agent makes use of the norm identification framework. The paper is organized as follows. Section 2 provides a background on normative multi-agent systems (NorMAS). Section 3 provides an overview of the norm identification framework.

Section 3 describes the required technologies for the solution approach. The proposed agent-oriented architecture is discussed in section 4 followed by a working example in section 5. Section 6 concludes with a brief summary and an outlook. 2 Information Retrieval Process in Industrial Automation Systems Industrial automation systems such as those deployed in large industrial plants are known to be complex hardware and software systems. The automation system used within an industrial plant consists of distributed and heterogeneous data sources which are accessed via specific applications.

What an agent does with the norms once it has inferred the norms is out of the scope of this paper. 4 Obligation Norm Identification In this section we explain how obligation norms can be identified using the framework described in Section 3. Obligation norms can be identified by an agent in our architecture using obligation norm inference (ONI) algorithm proposed here. First, we describe the domain in which an obligation norm is identified. Second, we describe the event storage components of the architecture (steps 2 and 3 of Figure 1).

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