Abstract
This paper addresses the issues of explainability of case-based support systems, particularly structural CBR systems dominated by knowledge-rich comprehensive cases and domain models. We show how explanation patterns and contextually enriched explanations of retrieval results can provide human-understandable insights on the system behavior, justify the shown results, and recommend the best cases to be considered for further use. We applied and implemented our approach as an agent-based system module within a case-based assistance framework for support of the early conceptual phases in architectural design, taking a single floor plan as a case with a high number of attributes. For the retrieval phase, a semantic search pattern structure, Semantic Fingerprint, was applied, whereas the explanation generation phase is controlled by a number of explanation patterns adapted from already existing explanation goals. Rulesets, case bases, and natural language generation are used for construction and automatic revision of explanation expressions. A contextualization feature categorizes the results into different context classes and includes this information into the explanation. A user study we conducted after the implementation of the explanation algorithm resulted in good acceptance by the representatives of the architectural domain, a quantitative experiment revealed a high rate of valid generated explanations and a reasonable distribution of patterns and contexts.
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Eisenstadt, V., Espinoza-Stapelfeld, C., Mikyas, A., Althoff, KD. (2018). Explainable Distributed Case-Based Support Systems: Patterns for Enhancement and Validation of Design Recommendations. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_6
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