The MYCIN Experiments of the Stanford Heuristic Programming Project

By Bruce G. Buchanan. 1985

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “ seen a resurgence, after having slowed since the burst of work on explanation in expert systems over three decades ago; for example, see Chandrasekaran et al. [23], [168], and Buchanan and Shortlie [14]. Sometimes abbreviated XAI (eXplainable articial intelligence), the idea can be found in grant s”
Efficient Search for Diverse Coherent Explanations (2019, background): “The desire for explanations of how complex computer systems make decisions dates back to some of the earliest work on expert systems (Buchanan and Shortliffe, 1984).”
Rule extraction from linear support vector machines (2005, background): “Much research in the field of artificial intelligence, and now knowledge discovery and data mining has focused on the endowing systems with the ability to explain their reasoning, both to make the consultation more acceptable to the user, and to help the human expert more easily identify errors in t”
Designing and Evaluating Explanations for Recommender Systems (2011, methodology): “Explanations in intelligent systems are not a new idea: explanations have often been considered as part of the research in the area of expert systems [8, 32, 38, 27 , 66].”
Bayesian Artificial Intelligence (2004, background): “One of the major advantages of Bayesian networks over most alternative uncertainty formalisms (such as PROSPECTOR [78] and Certainty Factors [34]) is that Bayesian networks allow, but do not require, conditional independencies to be modeled.”
Aggregation Functions: A Guide for Practitioners (2007, methodology): “In some early expert systems (MYCIN [38] and PROSPECTOR [88]) the [−1, 1] scale was used.”
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity (2007, unknown): “These systems have been successfully applied to tasks such as medical diagnosis [ 5 ] and helping chemists identify unknown organic molecules [16].”
Artificial intelligence in retina. (2018, methodology): “The largest established ROP screening program is the SUNDROP (Stanford University Network for Diagnosis of Retinopathy of Prematurity) program established in 2008 by Moshfeghi et al. and followed up in publications for 6 years (Murakami et al., 2008; Wang et al., 2015).”