A Review of Explanation and Explanation in Case-Based Reasoning

By Dónal Doyle, Alexey Tsymbal, Padraig Cunningham. 2003

62 citations recorded by the source corpus.

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…Chakraborty et al., 2017; Clancey, 1986; Core et al., 2006; Craven and Shavlik, 1996; Doran, Shulz, and Besold, 2017; Doshi-Velez and Kim, 2017ab; Doyle, Tsymbal, and Cunningham, 2003; Ellis, 1989; Fox, Long, and Magazzeni, 2017; Gilpin, et al., 2018; Hoffman and Klein, 2017; Hoffman, Mueller,…”
Explaining Recommendations (2007, unknown): “Explanations in intelligent systems began with expert systems which were predominantly based on heuristics [1], but also on case-based reasoning (CBR) [2], and model based approaches [3].”
Defining Explainable AI for Requirements Analysis (2018, unknown): “Some say they are not ex-plainable [9,23] while others claim that it is quite possible to generate explanations from them [21,14].”
A Survey of Explanations in Recommender Systems (2007, background): “The history of explanations in intelligent systems began with expert systems which were predominantly based on heuristics [7], but also on case-based reasoning (CBR) [12], and model based approaches [13].”
A historical perspective of explainable Artificial Intelligence (2020, background): “…why a certain hypothesis holds based on a knowledge base — usually encoded as a set of production or symbolic rules — and an inference mechanism, such as deduction, abduction, or analogical reasoning (Doyle, Tsymbal, & Cunningham, 2003; Lacave & Diez, 2004; Mitchell, Keller, & Kedar-Cabelli, 1986).”
Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems (2014, methodology): “Explanations for recommendation are predominantly based on heuristics(Tintarev and Masthoff 2007), but there are also works based on case-based reasoning(Doyle, Tsymbal, and Cunningham 2003) and model based approaches(Lea, Spears, and de Groot 2001).”