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Introduction to Coefficient of Variation
Published in K. Hima Bindu, M Raghava, Nilanjan Dey, C. Raghavendra Rao, Coefficient of Variation and Machine Learning Applications, 2019
K. Hima Bindu, M Raghava, Nilanjan Dey, C. Raghavendra Rao
Expert systems (decision support system and knowledge support system) provided tools for decision-making by encapsulating rules and built appropriate inference engines more in medical domain (Mycin [1] is an example). The tractability limitations of building knowledge and inference engines came in the way of growth in Artificial Intelligence and Expert Systems. Technological growth in databases, Data Mining, and pattern recognition tools gave a new avenue to perform knowledge discovery in databases. This led rule generation and knowledge representation in an automated manner. With an assumption that data instance is an apt representative for a particular domain, the knowledge and rules discovered can be treated as domain knowledge, which can be utilized for building inference engines. Inference engines are mainly for making decisions. Thus, a recommender system is analogous to the decision support system. As this process is around data instance, these methods are called data-centric methods.
Expert Systems Applied to Spacecraft Fire Safety
Published in Paul R. DeCicco, Special Problems in Fire Protection Engineering, 2019
Richard L. Smith, Takashi Kashiwagi
One example of an expert system is MYCIN [8], which diagnoses and recommends treatment for infectious diseases. In comparisons with medical experts in this limited domain of infectious diseases, the performance of MYCIN is shown to be as good as that of the human experts. Another well-known expert system is DENDRAL [9], that uses primarily mass spectrographic and nuclear magnetic resonance data to determine the molecular structure of unknown compounds. DENDRAL’s performance is superior to most human experts in its domain. Finally, the expert system that is possibly the greatest commercial success is XCON (or R1 as it was originally called) [10]. It is used by Digital Equipment Corporation to configure computer systems for its customers. Because of the large number of possible combinations of computer components, the problem of getting all the components, cables, etc. together to assemble a working system without missing a part or having items left over is very complex. XCON locates all parts in a reasonable arrangement and plans all the connections. It also verifies that the customer order is correct in that there are no missing nor surplus parts. It has been in constant use since 1980, and its performance has been significantly superior to that of human experts.
Expert Systems for Fault Diagnosis
Published in José Manuel Torres Farinha, Asset Maintenance Engineering Methodologies, 2018
The areas of application of expert systems are diverse, such as the following: MYCIN—A classic of expert systems developed at Stanford University in the 1970s at the medical school to diagnose hospital infections. About 2,000,000 people/year in the United States are affected by such problems when they remain hospitalized. Of these, about 50,000 die.XCON—A system where the Digital Equipment Corporation (DEC) designs customized minicomputer configurations and manages order processing in the manufacturing and distribution sectors.CATS—A system that operates at General Electric (GE) to diagnose failures in diesel-electric locomotives, implemented in the 1980s to minimize GE's dependence on the services of its former chief engineer.
Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels
Published in International Journal of Human–Computer Interaction, 2023
Pelin Uymaz, Ali Osman Uymaz, Yakup Akgül
AI, often known as “computational intelligence” or “the science and engineering of constructing intelligent machines” refers to the rapidly expanding discipline of emulating intelligence (Radanliev & De Roure, 2022b), human-like behavior in computers and other technologies (Amisha et al., 2019; Ebermann et al., 2023). In 1950, Alan Turing, one of the pioneers of contemporary computers and AI, developed the “Turing test” which was based on the idea that intelligent behavior in a machine is the capacity to do cognition-related activities at a level comparable to that of a human (Mintz & Brodie, 2019). Edward Shortliffe created the MYCIN AI system in the 1970s, which was used to diagnose blood-borne bacterial infectious diseases and recommend the use of antibiotics (Guo & Li, 2018). Since then, disease diagnosis and treatment have been a focus of AI technology (Davenport & Kalakota, 2019).
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities
Published in Information Systems Management, 2022
Christian Meske, Enrico Bunde, Johannes Schneider, Martin Gersch
Symbolic AI such as MYCIN, an expert system to diagnose and recommend treatment for bacteria-related infections in the 1970s (Fagan et al., 1980), was already able to explain its reasoning for diagnostic or instructional purposes. However, to the best of our knowledge, it took until 2002, when the term “Explainable Artificial Intelligence” was mentioned the first time as a side-note in a review of “Full Spectrum Command” (FSC, Brewster, 2002), a PC-based military simulation of tactical decision making. In this review of a preliminary beta version of FSC, which was still a GOFAI knowledge-based system, XAI referred to the feature that it “can tell the student exactly what it did and why” (Brewster, 2002, p. 8), consequently augmenting the instructor-facilitated after-action review. Two years later, FSC was presented by their developers in an article at the computer science conference on Innovative Applications of Artificial Intelligence, in which FSC was described as an “XAI System” for small-unit tactical behavior (Van Lent et al., 2004). In this paper, XAI systems were officially introduced and defined as systems that “present the user with an easily understood chain of reasoning from the user’s order, through the system’s knowledge and inference, to the resulting behavior” (Van Lent et al., 2004, p. 900).
Automating versus augmenting intelligence
Published in Journal of Enterprise Transformation, 2018
William B. Rouse, James C. Spohrer
The 1970s saw applications of AI to enhance medical diagnosis and treatment, starting perhaps with MYCIN (Shortliffe & Buchanan, 1975). However, a report by James Lighthill (1973) criticized AI for articulating and then failing in its pursuit of grandiose objectives. This report, and other forces, led to the First AI Winter, with substantial DARPA funding cuts.