An expert system shell to teach problem solving

TechTrends ◽  
1988 ◽  
Vol 33 (2) ◽  
pp. 22-26 ◽  
Author(s):  
Renate C. Lippert
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert System Shell

Experience with a Functional-Logic Multi-Agent Architecture for Medical Problem Solving

2011 ◽  
pp. 17-37
Author(s):  
Giordano Lanzola ◽  
Harold Boley
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Medical Care ◽  
Medical Problem ◽  
Agent Architecture ◽  
Medical Care System ◽  
Expert System Shell ◽  
Original Sense ◽  
Functional Logic ◽  
Multi Agent

The present paper reports on our experience with applying RELFUN (Boley, 1999) to problems in distributed medical care. This application arose “externally” in the original sense of the word: Massimiliano Campagnoli, working with the LISP-based expert-system shell KEE, supporting frames as well as forward and backward rules, noticed RELFUN on the net and switched to it, since frames are mappable into clauses and RELFUN’s rules offer more versatility than KEE’s. After his initial implementation of a RELFUN-based distributed medical-care system, he contacted the second author, further developing RELFUN, and the Pavia/Kaiserslautern teams joined forces, with the first author also being the expert in the medical domain.

A pedagogic expert system shell in Prolog

1988 ◽  
Vol 23 (6) ◽  
pp. 35-38
Author(s):  
Victor Schneider
Keyword(s):  
Expert System ◽  
Expert System Shell

Treatment of Uncertainty in an Oncology Protocol by Probabilistic and Artificial Intelligence Approaches

1988 ◽  
Vol 27 (01) ◽  
pp. 23-33 ◽  
Author(s):  
Fiorella de Rosis ◽  
G. Steve ◽  
C. Biagini ◽  
R. Maurizi-Enrici
Keyword(s):  
Artificial Intelligence ◽  
Sensitivity Analysis ◽  
Expert System ◽  
Probabilistic Model ◽  
Decision Process ◽  
Clinical Decision ◽  
Final Diagnosis ◽  
Decision Models ◽  
Expert System Shell ◽  
Subjective Estimation

SummaryThe decision process for diagnosis and treatment of Hodgkin’s disease at the Institute of Radiology of Rome has been modelled integrating the guidelines of a protocol with uncertainty aspects. Two models have been built, using a PROSPECTOR-like Expert System shell for microcomputers: the first of them treats the uncertainty by the inferential engine of the shell, the second is a probabilistic model. The decisions suggested in a group of simulated and real cases by a section of the two models have been compared with an “objective” final diagnosis; this analysis showed that, in some cases, the two models give different suggestions and that “approximations” of the shell’s inferential engine may induce wrong conclusions. A sensitivity analysis of the probabilistic model showed that the outputs are greatly influenced by variations of parameters, whose subjective estimation appears to be especially difficult. This experience gives the opportunity to consider the risks of building clinical decision models based on Expert System shells, if the assumptions and approximations hidden in the shell have not been previously analyzed in a careful and critical way.

scholarly journals Identifying Severe Malnutrition in Children with an Expert System

2021 ◽  
pp. 15-20
Author(s):  
Devia Kartika ◽  
Rima Liana Gema ◽  
Mutiana Pratiwi
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Computer Program ◽  
System Application ◽  
Nutrition Status ◽  
Easy Access ◽  
Severe Malnutrition ◽  
Inference Method ◽  
Human Expert ◽  
System Method

Expert system is a computer program which is designed for modelling the ability of problem solving as it is an expert (human expert). The expert system method used is the forward chaining method which is the inference method that is doing logical reasoning from the problem to its solution. The aim of this research is to design and develop an expert system that is able to identify the severe malnutrition on children from the age of 0 - 5 years old. The knowledge is derived from the question askedto a nutrition expert. The data are taken from the questions asked to the user and when all of the questions has been answered, then the goal will be appeared which shows the nutrition status. This system application will enable the user to diagnose the nutrition/disease that affects children and get the solution. This system can be used by any kind of user due to the easy access. This system is also put the important information about the severe malnutrition and the recent news of children’s health so it will add more knowledge for the parents about the importance of severe malnutrition’s prevention.

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