Using scenarios to explain probabilistic inference

By M. Druzdel, Max Henrion. 1990

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, methodology): “…and Halpern, 1997; Chan and Darwiche, 2012; Chang, Gerrish, Wang, Boyd-Graber, and Blei, 2009; Doyle et al., 2003; Druzdzel, 1990, 1996a, 1996b; Druzdzel and Henrion, 1990; Eaves Jr and Shafto, 2016; Fox, Glasspool, Grecu, Modgil, South, and Patkar, 2007; Freuder, Likitvivatanavong, and…”
A review of explanation methods for Bayesian networks (2002, background): “According to Druzdzel and Henrion (Druzdzel, 1993; Druzdzel & Henrion, 1990; Henrion & Druzdzel, 1990), a scenario is an assignment of values to variables that are relevant to a certain conclusion, ordered in such a way that they form a coherent story – a causal story, if possible – compatible with…”
Qualitative Verbal Explanations in Bayesian Belief Networks (1996, background): “Of verbal explanations, two types addressed large models (as opposed to local interactions): scenario-based approach [4, 11] and belief propagation-based approach [11, 21].”