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 lawsuits of the sixth foreign Workshop on brokers and knowledge Mining interplay, ADMI 2010, held in Toronto, Canada, in may well 2010. The 15 revised complete papers awarded have been conscientiously reviewed and chosen from 37 submissions. The papers are equipped in topical sections on brokers for info mining; information mining for brokers; information mining in brokers; and agent mining purposes.
Read Online or Download 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 PDF
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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
3. The User Interaction Layer, that implements flexible user interfaces and manages the user interaction by receiving search queries and passing search results to the user. Inside the multi-agent system, the number and composition of agents depend on the number of queries and vary over time. Fig. 5 depicts a sequence diagram that illustrates the corresponding search steps which are accomplished by the multi-agent system. It is worth noting that there can be several instances of agent types and different ontologies involved in the query process.
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. Fig. 1 shows a typical structure of information elements in an industrial automation environment.
A Data Mining Approach to Identify Obligation Norms 47 had happened in the past (or that had not happened in the past) may have triggered the occurrence of the special event4. The invocation of the norm inference component may result in the identification of a g-norm, in which case it is added to the g-norm base. An agent, being an autonomous entity, can also decide not to invoke its norm inference component for every occurrence of a special event but may decide to invoke it periodically. When it invokes the norm inference component, it may find a new g-norm which it adds to its g-norm base.
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.)