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

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

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Additional info 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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It is difficult to reach optimal outcomes in bilateral or multi-lateral negotiations over multiple resources when the agents’ preferences for the resources are not common knowledge. Self-interested agents often end up negotiating inefficient agreements in such situations. We have developed a 38 S. Sen et al. protocol for negotiation over multiple indivisible resources which can be used by rational agents to reach efficient outcomes [13]. Our proposed protocol enables the negotiating agents to identify efficient solutions using systematic distributed search that visits only a subspace of the whole solution space.

More specifically, agents will need mechanisms to benefit from complementary expertise in the group, pool together resources to meet new demands and exploit transient opportunities, negotiate fair settlements, develop norms to facilitate coordination, exchange help and transfer knowledge between peers, secure the community against intruders, and learn to collaborate effectively. In this talk, I will summarize some of our research results on trust-based computing, negotiation, and learning that will enable intelligent agents to develop and sustain robust, adaptive, and successful agent communities.

In addition, an agent with few neighbors will encounter few different behaviors from its neighbors, and it is a priori easier to coordinate with a small set of agents rather than a larger one. the decreasing interaction frequency between pairs of learners increases the time for exploration of the behavior space and thereby influences the learning patterns of the agents in the network. 6 Finding Partners Human and artificial agents routinely make critical choices about interaction partners. 5 1 0 100 200 300 400 500 600 700 800 900 1000 iterations Fig.

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