Vladimir Gorodetsky, Chengqi Zhang, Victor Skormin, Visit's Autonomous Intelligent Systems: Multi-Agents and Data PDF

Vladimir Gorodetsky, Chengqi Zhang, Victor Skormin, Visit's Autonomous Intelligent Systems: Multi-Agents and Data PDF

By Vladimir Gorodetsky, Chengqi Zhang, Victor Skormin, Visit Amazon's Longbing Cao Page, search results, Learn about Author Central, Longbing Cao,

This booklet constitutes the refereed court cases of the second one foreign Workshop on self sustaining clever structures: brokers and information Mining, AIS-ADM 2007, held in St. Petersburg, Russia in June 2007.

The 17 revised complete papers and six revised brief papers provided including four invited lectures have been rigorously reviewed and chosen from 39 submissions. The papers are equipped in topical sections on agent and knowledge mining, agent festival and information mining, in addition to textual content mining, semantic net, and agents.

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Read or Download Autonomous Intelligent Systems: Multi-Agents and Data Mining: Second International Workshop, AIS-ADM 2007, St. Petersburg, Russia, June 3-5, 2007, Proceedings PDF

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Additional resources for Autonomous Intelligent Systems: Multi-Agents and Data Mining: Second International Workshop, AIS-ADM 2007, St. Petersburg, Russia, June 3-5, 2007, Proceedings

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Edu/~sandip) for a list of our papers which would provide a more thorough and representative overview of our research. We will overview the following subset of our research areas: • In Section 2 we present results from our work on trusted, reciprocal relationship maintenance in agent communities and its applications in P2P networks. • In Section 3 we present a multiagent learning scheme that solve well-known social dilemma problems like the Prisoner’s dilemma, • In Section 4 we present a framework by which one agent in the community can teach classification knowledge to another agent without knowledge of the latter’s knowledge representation or learning algorithms.

While effective norms and social conventions can significantly enhance performance of individual agents and agent societies and have merited in-depth studies in the social sciencethere has been little work in multiagent systems on the formation of social norms. We have recently used a model that supports the emergence of social norms via learning from interaction experiences. In our model, individual agents repeatedly interact with other agents in the society over instances of a given scenario [10].

Then the ratio of a number of semantic relations of a particular object to the average number for the whole document determines the “contrast ratio” of this object in the document. This additional index is important to the analysts who evaluate the semantic intensity of documents. The system allows adjusting the relevance threshold value of documents in an navigation card, thus changing a number of documents returned by the system. The threshold value equal to 100 % means that a given object has a maximum number of revealed semantic links, and therefore is a candidate for the main topic of the document.

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