By Balázs Kégl, Guy Lapalme
This booklet constitutes the refereed lawsuits of the 18th convention of the Canadian Society for Computational experiences of Intelligence, Canadian AI 2005, held in Victoria, Canada in may well 2005.
The revised complete papers and 19 revised brief papers offered have been rigorously reviewed and chosen from a hundred thirty five submission. The papers are geared up in topical sections on brokers, constraint pride and seek, info mining, wisdom illustration and reasoning, computer studying, common language processing, and reinforcement studying.
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Additional info for Advances in Artificial Intelligence: 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria,
6. [Agent] Split the data, according to the best global attribute and its associated split value, in the formation of two separate clusters of data in the selected agent. 7. [Agent] Distribute the structural information in each cluster and the best attribute to the other agents through the mediator. 8. [Agent] Construct the partial decision trees according to the structural information in other agents. 9. [Agent] Generate decision rules at each agent and notify the mediator for termination if there is no more splitting.
ARES 2 achieves this by implementing different so-called world rules that describe how effects in the environment can be or have to be achieved by the agents. This is possible in ARES 2, because of the rather simplified worlds that agents are acting in within ARES 2. ARES 2: A Tool for Evaluating Cooperative and Competitive Multi-agent Systems 41 By combining the selected world rules for different world features, the resulting environment will require rather different strategies by the agents to be successful.
This approach is less expensive but may produce ambiguous and incorrect global results. To make up for such a weakness, many researchers have spent great efforts looking for more advanced approaches of combining local models built at different sites. Most of these approaches are agent-based high level learning such as meta-learning , knowledge probing , B. Kégl and G. ): AI 2005, LNAI 3501, pp. 25 – 32, 2005. © Springer-Verlag Berlin Heidelberg 2005 26 S. Baik, J. Cho, and J. Bala and mixture of experts , Bayesian model averaging , and stacked generalization .
Advances in Artificial Intelligence: 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, by Balázs Kégl, Guy Lapalme