Qualitative Verbal Explanations in Bayesian Belief Networks

By Marek J Druzdzel. 1996

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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): “(Chajewska 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…”
Bayesian Artificial Intelligence (2004, methodology): “They have been shown to support the detection of possible errors in the model [97] and can be used by the domain expert to verify the output of the model [271, 178, 77].”
A review of explanation methods for Bayesian networks (2002, methodology): “Causal Bayesian networks support certain qualitative reasoning patterns, which can be identified in order to explain the results of inference (Druzdzel, 1993; Druzdzel, 1996; Henrion & Druzdzel, 1990).”
Graphical Explanation in Belief Networks (1997, background): “Chamberlain and Nordahl (1988), Cooper (1989), Druzdzel (1996), Henrion and Druzdzel (1990), Lauritzen and Spiegelhalter (1988), Pearl (1987), Suermondt (1991), and Suermondt and Cooper (1993) and have all made suggestions as to how such explanations might be generated.”
Learning Bayesian networks: approaches and issues (2011, background): “Other authors have looked at the structure of the clique tree; Kjærulff (1997) shows how the cliques in the tree may themselves be factored into a clique tree, and Darwiche (1998) shows how to keep clique trees up to date after pruning irrelevant parts of the network.”
Explanation in Probabilistic Systems: Is It Feasible? Will It Work? (2001, background): “These signs are then translated into verbal form [8, 10, 17].”
On the Use of Opinionated Explanations to Rank and Justify Recommendations (2016, methodology): “…have been used to support intelligent reasoning systems for some time; for instance, there are examples based on heuristics (Buchanan and Shortliffe 1984), case-based reasoning (Sørmo, Cassens, and Aamodt 2005; McSherry 2004; Doyle et al. 2004), and model-based techniques (Druzdzel 1996).”
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 two-phase method for extracting explanatory arguments from Bayesian networks (2017, unknown): “See for instance the work of Lacave905 and Dı́ez [12, 40], Koiter [13] and Druzdzel [41].”
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].”