Knowledge representation using an augmented planning network: application to an expert system for planning HPLC separations

1988 ◽  
Vol 28 (2) ◽  
pp. 82-86 ◽  
Author(s):  
A. L. Ananda ◽  
S. M. Foo ◽  
Hari Gunasingham
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Network Application

Building an Expert System for Engineering Design Based on the Object-Oriented Knowledge Representation Concept

1988 ◽  
Author(s):  
S. Akagi ◽  
K. Fujita
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Engineering Design ◽  
Design Process ◽  
Model Building ◽  
Object Oriented ◽  
Design Model ◽  
Design Problems ◽  
Design Variables ◽  
Effective Diagnosis

Abstract An expert system is developed for engineering design based on object-oriented knowledge representation concept. The design process is understood as determining design variables and their relationships which compose design model. The design model is represented as a network in the computer system using the object-oriented knowledge representation. The system built with the above concept provides the following abilities: 1) flexible model building and easy modification, 2) effective diagnosis of the design process, 3) supporting method for redesign, 4) a hybrid function with numerical computations and graphics, and 5) applicability for various design problems. Finally, it is applied to the basic design of a ship.

Logic Programming Languages for Expert Systems

2011 ◽  
pp. 593-603
Author(s):  
James D. Jones
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Logic Programming ◽  
Programming Languages ◽  
Default Reasoning ◽  
Enterprise Information ◽  
Expert System Shell ◽  
Closed World ◽  
Logic Programming Languages

“Expert systems” are a significant subset of what is known as “decision support systems” (DSS). This article suggests a different paradigm for expert systems than what is commonly used. Most often, expert systems are developed with a tool called an “expert system shell.” For the more adventurous, an expert system might be developed with Prolog, a language for artificial intelligence. Both Prolog and expert system shells stem from technology that is approximately 30 years old.1 There have been updates to these platforms, such as GUI interfaces, XML interfaces, and other “bells and whistles.” However, the technology is still fundamentally old. As an analogy, the current technology is akin to updating a 30-year-old car with new paint (a gooey interface), new upholstery, GPS, and so forth. However, the car is fundamentally still a 30-year-old car. It may be in far better shape than another 30-year-old car without the updates, but it cannot compete from an engineering perspective with current models.2 Similarly, the reasoning power of current expert system technology cannot compete with the reasoning power of the state of the art in logic programming. These advances that have taken place in the logic programming community since the advent of Prolog and expert system shells include: a well developed theory of multiple forms of negation, an understanding of open domains, and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e., a solution to the frame problem and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously (i.e., reasoning with uncertainty). This article examines a family of logic programming languages. This article in conjunction with a companion article this volume, Knowledge Representation That Can Empower Expert Systems, suggest that logic programs employing recent advances in semantics and in knowledge representation provide a more robust framework in which to develop expert systems. The author has successfully applied this paradigm and these ideas to financial applications, security applications, and enterprise information systems.

scholarly journals “Virtual Doctor” Management Technique in the Diagnosis of ENT Diseases

2019 ◽  
Vol 15 (09) ◽  
pp. 88
Author(s):  
Arshi Naim ◽  
Mohammad Faiz Khan ◽  
Mohammad Rashid Hussain ◽  
Nawsher Khan
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Knowledge Representation ◽  
Daily Practice ◽  
Management Technique ◽  
Rule Based ◽  
Rule Based System ◽  
Related Disease ◽  
Effectiveness And Efficiency ◽  
Early Diagnose

<p>This research project is about the managing of an ENT ( Ear Nose Throat) diagnosis expert system through the virtual doctor that can assist physicians in diagnosing ENT related diseases. ENT problems can distress hearing, speaking, learning and many other significant behaviors and untreated ENT diseases can be serious. Therefore, early diagnose of ENT diseases is fundamental. This study is qualitative in nature where we have used the concept of Virtual Doctor under artificial intelligence (AI) based expert system, already designed to assist physicians in the diagnosis of ENT related disease in the absence of ENT experts. This system can reduce the excess created due to the busy schedules of ENT experts and enhance the effectiveness and efficiency of healthcare system. Virtual Doctor for ENT diagnosis uses rule based system for knowledge representation and has sub-systems which can enhance the physician’s ability in reaching a diagnosis decision with assurance. In this paper, we described the management system for application of Virtual Doctor for the diagnosis of  ENT related diseases which can be used by physicians in their daily practice.</p>

Design of Fault Diagnosis Expert System of Transformer

2013 ◽  
Vol 291-294 ◽  
pp. 2557-2561
Author(s):  
Tao Sun ◽  
Hai Bo Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
System Design ◽  
System Of Systems ◽  
Knowledge Representation And Reasoning ◽  
Design Knowledge ◽  
Systems Architecture ◽  
Fault Diagnosis System ◽  
Transformer Fault

The transformer fault diagnosis expert system design knowledge representation and reasoning mechanisms are the key issue. Characteristics of transformer fault diagnosis system based on human experts, learning on the basis of the human expert diagnosis of transformer faults, to build a transformer fault diagnosis expert system of systems architecture, knowledge representation and reasoning mechanisms for a more detailed analysis and discussion.

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