Publikation

Explainable Distributed Case-based Support Systems: Patterns for Enhancement and Validation of Design Recommendations

Viktor Eisenstadt, Christian Espinoza-Stapelfeld, Ada Mikyas, Klaus-Dieter Althoff

In: Michael T. Cox, Peter Funk, Shahina Begum (Hrsg.). Case-Based Reasoning Research and Development. International Conference on Case-Based Reasoning (ICCBR-2018) July 9-12 Stockholm Sweden Springer 12/2018.

Abstrakt

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. Rule sets, 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.

Weitere Links

iccbr2018_camready.pdf (pdf, 887 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence