Report on the 24th International Conference on Case-Based Reasoning Research and Development (ICCBR-2016)
remembering and adapting solutions previously used to solve similar problems. CBR favors incremental learning from experience and acquisition of expertise rather than exhaustive extraction of domain knowledge. Research on CBR appears to be gaining momentum because of several reasons: new application domains, a closer alliance with research on analogy and creativity, hybrid approaches for the different CBR processes, a renewed interest on textual and conversational systems, management of big case bases, and explanation, transparency, and trust of the reasoning results based on the underlying examples. Conference Report
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Report on the 24th International Conference on Case-Based Reasoning Research and Development (ICCBR-2016)
Semantic Scholar · Computer Science · 2017
Abstract
remembering and adapting solutions previously used to solve similar problems. CBR favors incremental learning from experience and acquisition of expertise rather than exhaustive extraction of domain knowledge. Research on CBR appears to be gaining momentum because of several reasons: new application domains, a closer alliance with research on analogy and creativity, hybrid approaches for the different CBR processes, a renewed interest on textual and conversational systems, management of big case bases, and explanation, transparency, and trust of the reasoning results based on the underlying examples. Conference Report