Rule-based expert systems: The mycin experiments of the stanford heuristic programming project

By William R. Swartout. 1985

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Citations in the corpus

Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences (2017, neutral): “, 1989], [Swartout and Moore, 1993], and [Buchanan and Shortliffe, 1984].”
A historical perspective of explainable Artificial Intelligence (2020, methodology): “Explanations in these systems consist of either descriptions coupled to the reasoning trace of the system, or descriptions more coupled to the story behind the decision-making process of the system (Buchanan & Shortliffe, 1984; Wick & Thompson, 1992).”
A (Very) Brief History of Artificial Intelligence (2005, background): “Mycin (Buchanan and Shortliffe 1984) and the thousands of expert systems following it became visible demonstrations of the power of small amounts of knowledge to enable intelligent decision-making programs in numerous areas of importance.”
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, background): “The 1970s saw a signiﬁcant leap forward with the emergence of rule-based expert systems like MYCIN, a seminal creation by Buchanan and Shortliffe [23,24].”
Explaining and justifying the advice of a decision support system: a natural language generation approach (2003, methodology): “Rule-based systems such as MYCIN cannot justify their rules (Clancey, 1983).”
Machine Learning and Radiology (2012, background): “It is an expert system with about 600 rules designed to identify bacteria and recommend antibiotics (Swartout, 1985).”
A Tractable Inference Algorithm for Diagnosing Multiple Diseases (2013, background): “One of the most common criticisms of the use of probability theory in expert systems is that the theory is impractical to apply in realistic situations [Buchanan and Shortliﬀe, 1984].”
Evidential reasoning rule for evidence combination (2013, background): “D–S theory was originally investigated in the 1960’s [4,5], formalised in the 1970’s [25] and has been researched ever since [3,36,26,29,38,23,41,16,33,34,14,8].”
From certainty factors to belief networks (1992, background): “For the sake of brevity, we will not describe other variations, but they are thoroughly outlined in [4].”
Agents that Learn to Explain Themselves (1994, background): “Potential users of computer-generated forces therefore attach great importance to explanation, just as potential users of computer-based medical consultation systems do (Teach & Shortliffe 1984).”
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].”
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).”
Great Explanations: Opinionated Explanations for Recommendations (2015, methodology): “There is a history of using explanations to support reasoning in intelligent systems with approaches based on heuristics [9], CBR [10–12], and model-based”
Watson: Beyond Jeopardy! (2013, background): “The MYCIN system for diagnosing infectious diseases, also developed in the 1970s, used structured knowledge in the form of production rules stating that, if certain facts are true, then one can conclude certain other facts with a given certainty factor [4].”
Stakeholders in Explainable AI (2018, background): “The issue ﬁrst came to prominence during the ‘knowledge engineering era’ of the late 1970s and early 1980s, when the focus was on building expert systems to emulate human reasoning in specialist high-value domains such as medicine, engineering and geology (Buchanan and Shortliffe 1984).”
Introduction to the Special Issue on Explanation in Case-Based Reasoning (2005, background): “The importance of intelligent systems having the ability to explain their reasoning is well recognized (e.g., Buchanan and Shortliffe 1984; Southwick 1991; Swartout et al., 1991; Leake 1996; Herlocker et al., 2000; McSherry 2001).”
Asking 'Why' in AI: Explainability of intelligent systems - perspectives and challenges (2018, methodology): “In the former case, a user could ask HOW in response to a conclusion, and receive a trace of the rules fired, along with the certainty factors (Buchanan and Shortliffe, 1984).”