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

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

The 18th convention of the Canadian Society for the Computational research of Intelligence (CSCSI) endured the good fortune of its predecessors. This set of - pers re?ects the range of the Canadian AI neighborhood and its overseas companions. AI 2005 attracted one hundred thirty five top of the range submissions: sixty four from Canada and seventy one from world wide. of those, 8 have been written in French. All submitted papers have been completely reviewed through not less than 3 participants of this system Committee. a complete of 30 contributions, approved as lengthy papers, and 19 as brief papers are integrated during this quantity. We invited 3 distinct researchers to offer talks approximately their present study pursuits: Eric Brill from Microsoft learn, Craig Boutilier from the collage of Toronto, and Henry Krautz from the college of Washington. The association of this sort of winning convention bene?ted from the coll- oration of lots of individuals. superior, we want to precise our apprec- tion to this system Committee contributors and exterior referees, who supplied well timed and signi?cant reports. to control the submission and reviewing approach we used the Paperdyne approach, which used to be built through Dirk Peters. We owe detailed due to 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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Extra 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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Adjusting the Autonomy of Collections of Agents in Multiagent Systems 37 John’s proxy agent (denoted as P roxy). There are currently no conflicting locks on John, so P roxy grants the lock to AgentA . This guarantees to AgentA that John will be free at time [3,10] and moreover, that John will have the expected bother cost (ie: John will not have been bothered earlier than time point 3). Now suppose AgentB reasons a strategy sB involving transferring to John at time point [15,20]. AgentB requests a lock on John from P roxy.

Kégl and G. ): AI 2005, LNAI 3501, pp. 38–42, 2005. c Springer-Verlag Berlin Heidelberg 2005 ARES 2: A Tool for Evaluating Cooperative and Competitive Multi-agent Systems 39 The components of the environment have been simplified down to what we consider to be the basic features relevant for multi-agent systems. As such we do not concentrate upon providing an environment to simulate real world scenarios, but one that concentrates on providing an area for demonstrating a range of different concepts.

1 Introduction Computational efficiency and scalability is a very critical issue in data mining [1] since the amount of data is rapidly increasing in the real world. Also, it is very important in data mining to deal with huge amounts of data located at different sites since these data are naturally located at geographically distributed sites, and some of them are relevant to each other. In such a distributed environment, a basic approach for data mining is to move all of the data to a central data repository and then to analyze them with a single data mining system.

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