Grid Computing and CBR Deployment: Monitoring Principles for a Suitable Engagement
This paper presents a mathematical technique for modeling the generation of Grid-solutions employing a Case based reasoning system (CBR). Roughly speaking, an intelligent system that tries to be adapted to highly dynamic environment needs an efficient integration of high-level processes (deliberative and time-costly) within low-level (reactive, faster but poorer in quality) processes. The most relevant aspect of our current approach is that, unexpectedly, the performance of the CBR-system do not get worse any time that it retrieves worse cases in situations even when it has enough time to generate better solutions. We concentrate on formal aspects of the proposed Grid-CBR system without establishing which should be the most adequate procedure in a subsequent implementation stage. The advantage of the presented scheme is that it does not depend on neither the particular problem nor a concrete environment. It consists in a formal approach that only requires, on one hand, local information about the averaged-time spent by the system in obtaining a solution and, on the other hand, an estimation about their temporal restrictions. The potential use of industry standard technologies to implement such a Grid-enabled CBR system is discussed here too.
KeywordsCase base reasoning Grid computing Artificial Intelligence
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- 1.Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI. Commun. 7(1), 39–59 (1994)Google Scholar
- 3.Berenji, H., Wang, Y., Saxena, A.: Dynamic Case Based Reasoning in Fault Diagnostics and Prognostics. In: FUZZIEEE, Reno (May 2005)Google Scholar
- 4.Cheng, D., Thawonmas, R.: Case-based plan recognition for realtime strategy games. In: Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education (CGAIDE 2004), pp. 36–40. University of Wolverhampton, Reading, UK (2004)Google Scholar
- 5.Dean, T., Boddy, M.: An analysis of time-dependent planning. In: Proc. of the 7th National Conference on Artificial Intelligence, pp. 49–54 (1988)Google Scholar
- 6.Eremeev, A., Varshavskiy, P.: Case-based Reasoning Method for Real-time Expert Diagnostics Systems. Institute of Information Theories and Applications FOI ITHEA 15(2) (2008) ISSN: 1313-0463Google Scholar
- 8.Goldman, R.P., Musliner, D.J., Krebsbach, K.D.: Managing Online Self-Adaptation in Real-Time Environments. In: Proc. of 2nd Int. Workshop on Self Adaptive Software (2001)Google Scholar
- 10.Julian, V.J., Botti, V.: Developing real-time multiagent systems. ICAE 11(2), 150–165 (2004)Google Scholar
- 11.Marling, M.T., Gillen, M., Alexander, D., Chelberg, D.: Case-based reasoning for planning and world modeling in the robocup small size league. In: Workshop on Issues in Designing Physical Agents for Dynamic Real-Time Environments, IJCAI (2003)Google Scholar
- 12.Ontanon, S., Mishra, K., Sugandh, N., Ram, A.: Line Case-Based Planning. Computational Intelligence, 84–119 (2010)Google Scholar