Download Advances in Artificial Intelligence: 18th Conference of the by Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.) PDF

By Adam Milstein (auth.), Balázs Kégl, Guy Lapalme (eds.)

The 18th convention of the Canadian Society for the Computational examine of Intelligence (CSCSI) endured the good fortune of its predecessors. This set of - pers re?ects the variety of the Canadian AI neighborhood and its foreign companions. AI 2005 attracted one hundred thirty five top of the range submissions: sixty four from Canada and seventy one from worldwide. of those, 8 have been written in French. All submitted papers have been completely reviewed by way of a minimum of 3 individuals of this system Committee. a complete of 30 contributions, approved as lengthy papers, and 19 as brief papers are incorporated during this quantity. We invited 3 extraordinary researchers to offer talks approximately their present learn pursuits: Eric Brill from Microsoft learn, Craig Boutilier from the college of Toronto, and Henry Krautz from the collage of Washington. The association of the sort of winning convention bene?ted from the coll- oration of many people. greatest, we want to specific our apprec- tion to this system Committee contributors and exterior referees, who supplied well timed and signi?cant stories. to regulate the submission and reviewing strategy we used the Paperdyne approach, which used to be built through Dirk Peters. We owe distinctive because of Kellogg sales space and Tricia d’Entremont for dealing with the neighborhood arrangementsandregistration.WealsothankBruceSpencerandmembersofthe CSCSI govt for all their e?orts in making AI 2005 a profitable conference.

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Additional resources for Advances in Artificial Intelligence: 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005. Proceedings

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Distributed constraint optimization as a formal model of partially adversarial cooperation. Technical Report CSE-TR-10191, Ann Arbor, MI 48109, 1991. 9. Makoto Yokoo and Katsutoshi Hirayama. Distributed constraint satisfaction algorithm for complex local problems. In Proceedings of the Third International Conference on Multiagent Systems (ICMAS-98), pages 372–379, 1998. 10. Lingzhong Zhou, John Thornton, and Abdul Sattar. Dynamic agent ordering in distributed constraint satisfaction problems. In Proceedings of the 16th Australian Joint Conference on Artificial Intelligence, AI-2003, Perth, 2003.

Agent] Find the attribute and its associated value that can best split the data into the various training classes during local mining. 3. [Agent] Send the best local attribute and its associated value to the mediator. 4. [Mediator] Select the best attribute from the best local attributes of all the agents. 5. [Mediator] Notify each agent of its role for the next action (splitting or waiting). 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.

Tcpu : CPU processing time for a component. The entropy computation time of SDM is as follows: RTsdm = Tsdm = k ⋅ p 2 ⋅ tcpu (1) On the other hand, since the given table is vertically partitioned into the same number of agents, the entropy computation time of DDM is as follows: Tddm = k ⋅ p 2 ⋅ tcpu (2) n When considering the communication time ( Tcom ), the transmission data amount becomes p ⋅ rc bits for the communication between each agent and the mediator. Therefore, the communication time is as follows: Tcom = p ⋅ rc ⋅ ttr (3) Through Equations of (2) and (3), the distributed processing time of DDM is as follows: RTddm = k ⋅ p 2 ⋅ t cpu n + p ⋅ rc ⋅ ttr (4) Here, we get to a point ( RTsdm − RTddm ≥ 0 ) where the performance of DDM becomes better than that of SDM.

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