|Título||Ceaseless case-based reasoning|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Martin F, Plaza E|
|Editor||Funk P, Calero PAGonzale|
|Conference Name||Lecture Notes in Computer Science|
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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