By Balázs Kégl, Guy Lapalme
This booklet constitutes the refereed court cases of the 18th convention of the Canadian Society for Computational stories of Intelligence, Canadian AI 2005, held in Victoria, Canada in could 2005.
The revised complete papers and 19 revised brief papers offered have been conscientiously reviewed and chosen from a hundred thirty five submission. The papers are geared up in topical sections on brokers, constraint pride and seek, information mining, wisdom illustration and reasoning, desktop studying, usual language processing, and reinforcement studying.
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Extra info for Advances in Artificial Intelligence: 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria,
A Decision Procedure for Autonomous Agents to Reason About Interaction With Humans. In The AAAI 2004 Spring Symposium on Interaction between Humans and Autonomous Systems over Extended Operation, pages 81–86, 2004. 3. C. Martin, D. Schreckenghost, and R. Bonasso. Augmenting Automated Control Software to Interact with Multiple Humans. In Proceeedings of AAAI04 Spring Symposium on Interaction Between Humans, 2004. 4. C. Micacchi. An Architecture for Multi-Agent Systems Operating in Soft Real-Time Environments With Unexpected Events.
In our approach , we allow an agent to possibly query a user for information, but still retain decision-making authority. In addition, as in , we incorporate a bother cost factor into the model, to account for the user’s annoyance at being interrupted by agents. , ask another query, or make a decision). Visually, one can imagine a strategy as a tree, with two types of nodes, query/internal nodes, and decision/leaf nodes. A query node will have several branches corresponding to the various possible responses, with each branch leading to a strategy subtree.
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.