Abstract
Most CBR systems try to solve problems in one shot neglecting the sequential behavior of most real world domains and the simultaneous occurrence of interleaved problems proper to multi-agent settings. This article provides a first answer to the following question: how can the CBR paradigm be enriched to support the analysis of unsegmented sequences of observational data stemming from multiple coincidental sources? We propose Ceaseless CBR, a new model that considers the CBR task as on-going rather than one-shot and aims at finding the best explanation of an unsegmented sequence of alerts with the purpose of pinpointing whether undesired situations have occurred or not and, if so, indicating the multiple responsible sources or at least which ones are the most plausible.
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Martin, F.J., Plaza, E. (2004). Ceaseless Case-Based Reasoning. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_22
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DOI: https://doi.org/10.1007/978-3-540-28631-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22882-0
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