A knowledge-intensive, integrated approach to problem solving and sustained learning

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The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, background): “CBR models designed explicitly to produce explanations [1] rely on the backward chaining of the causal relation from a solution, which does not scale as complexity increases.”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “CBR models designed explicitly to produce explanations [1] rely on the backward chaining of the causal relation from a solution, which may not scale as complexity increases.”
Explanatory Capabilities in the CREEK Knowledge-Intensive Case-Based Reasoner (2008, methodology): “The CREEK method [11,13], which is used in the work presented here, is a knowledge-intensive case-based reasoning architecture especially developed to approach problem-solving and learning in open and weak-theory domains.”
Explanation in Case-Based Reasoning–Perspectives and Goals (2005, methodology): “Semantic network representation of knowledge such as in the CREEK system (Aamodt, 1991; Sørmo and Aamodt, 2002) may provide some explanatory support showing the part of the network around the concept the user is interested in.”