By Balázs Kégl, Guy Lapalme
This publication constitutes the refereed court cases of the 18th convention of the Canadian Society for Computational experiences of Intelligence, Canadian AI 2005, held in Victoria, Canada in may possibly 2005.
The revised complete papers and 19 revised brief papers offered have been rigorously reviewed and chosen from one hundred thirty five submission. The papers are geared up in topical sections on brokers, constraint pride and seek, facts mining, wisdom illustration and reasoning, laptop studying, traditional 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,
Wang, Q. : On the privacy preserving properties of random data perturbation techniques, Proceedings of the Third IEEE International Conference on Data Mining, pp. 99-106, 2003 8. , Prodromidis, A. , Tselepis, S. : JAM: Java Agents for MetaLearning over Distributed Databases, Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 74-81, 1997 9. Guo, Y. : Knowledge probing in distributed data mining, In Advances in Distributed and Parallel Knowledge Discovery, 1999 32 S.
The ARES system has been used for the past three years at the graduate level –once at the undergraduate level– for courses on multi agent systems at the University of Calgary. Feedback from the students has shown a positive image of the ARES system. The system has provided a consistent way to compare the agents produced by different teams in the class. Each time the class has been taught different configurations of the world rules have been used to present the students with a different challenge while still working in the common rescue environment.
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.