Analysis of Expert System in the Field of Machine Design

2012 ◽  
Vol 479-481 ◽  
pp. 565-568
Author(s):  
Hong Qi Luo ◽  
Meng Yu Wang
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Inference Engine ◽  
Knowledge Bases ◽  
Machine Design ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Design Expert ◽  
Cad System

Intelligent CAD system can be formed if integrating the expert system and mechanical CAD. Components of expert system were analyzed, including integrated databases, knowledge bases, knowledge acquisition, inference engine, explanation mechanism and human-computer interface. The model of design-evaluate-redesign was introduced and discussed. Current situation of research on design expert systems was summarized.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Mivar databases and rules

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Knowledge Bases ◽  
Data Models ◽  
Active Network ◽  
New Model

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

Knowledge-acquisition tools with explicit problem-solving models

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Code Generation ◽  
System Development ◽  
Generalization Error ◽  
Domain Experts ◽  
The Past

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

The Design and Implementation of the Expert System for Cutting Parameters Based on CLIPS

2013 ◽  
Vol 385-386 ◽  
pp. 731-734
Author(s):  
Xue Bin Liu ◽  
Xi Bin Wang ◽  
Chong Ning Li ◽  
San Peng Deng
Keyword(s):  
Expert System ◽  
Cutting Parameters ◽  
Inference Engine ◽  
Knowledge Bases ◽  
Rule Base ◽  
Dynamic Link Library ◽  
Interactive Communication ◽  
Core Content ◽  
C Programming ◽  
Selection Of

Three core content of CLIPS expert system are researched on, namely rule base, fact base and inference engine. Using the production rules, in accordance with CLIPS syntax express the knowledge of cutting parameters, and according to the CLIPS syntax of the fact base, the rule base and the inference engine, knowledge bases of the cutting parameters expert system is built. In Visual C + + programming environment, combined with the CLIPS dynamic link library, by the interactive communication between Visual C + + and CLIPS, the software, cutting parameters expert system, has been developed successfully while has laid a good foundation for the selection of the cutting parameters to optimize.

A FUZZY DISTRIBUTED EXPERT SYSTEM DESIGN TOOL EXGEN AND ITS APPLICATION TO SENSORY FOODSTUFF EVALUATION

1999 ◽  
Vol 07 (04) ◽  
pp. 383-388
Author(s):  
THANH THUY NGUYEN ◽  
TOAN THANG NGUYEN ◽  
BINH CUONG THAC ◽  
DINH KHANG TRAN
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Bases ◽  
Design Tool ◽  
Rule Base ◽  
Production Rules ◽  
Linguistic Terms ◽  
University Of Technology ◽  
Fuzzy Expert Systems ◽  
And Control

Since the appearance of MYCIN, expert systems have been widely and successfully developed for various scientific and technological researches and applications. These applications require more and more fuzzy information resources because of the uncertainty, inexactness in labeling facts using linguistic terms and expressing human expertise. Sensory foodstuff evaluation is among this kind of fuzzy expert system applications. In the frame of the research project on fuzzy expert systems for science and technology at the Hanoi University of Technology, we have developed an expert system building tool called EXGEN which has the following features: – Knowledge editing in the form of production rules using Vietnamese in the natural language-like syntax. The tool is also capable to verify the consistency of an acquired knowledge base. – Inference engine consisting of two principal inference mechanisms (forward and backward inference) and control strategy module. We proposed also some heuristics for choosing a potential inference trace, allowing to get more information about conclusions. – Possibility of establishing a configuration for a distributed working session. It would be possible to carry out: + a deduction over a shared rule base (RB) in the server, based on information acquired from workstations (common RB and conclusion, distributed fact base (FB)) + a deduction over a shared RB in the server with different cognitive tasks (including hypotheses fact and conclusions) on workstations (common RB and distributed FB) + deductions on workstations with distributed knowledge bases (Distributed RB and FB) We have already implemented an application expert system SENEXSYS for sensory foodstuff evaluation using the building tool EXGEN. Experimental results have shown that qualification given by the expert system is comparable to evaluation results obtained by following up Vietnamese standard TCVN 3215.79

scholarly journals Issues in the design and implementation of expert systems

1987 ◽  
Vol 1 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Clive L. Dym
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Future Development ◽  
Stages Of Development ◽  
Task Selection ◽  
Design And Implementation ◽  
Institutional Issues ◽  
The Future ◽  
Run Time

This article discusses the issues that arise in the design and implementation of expert systems. These issues include: task selection; the stages of development of expert system projects; knowledge acquisition; languages and tools; development and run-time environments; and organizational and institutional issues. The article closes with some speculation about the future development of expert systems.

scholarly journals Penalaran Berbasis Aturan Untuk Diagnosa Awal Penyakit Anjing Menggunakan Teorema Bayes

2018 ◽  
Vol 2 (2) ◽  
pp. 530-535 ◽  
Author(s):  
Sella Marselena ◽  
Ause Labellapansa ◽  
Abdul Syukur
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Rural Areas ◽  
Medical Personnel ◽  
Bayes Theorem ◽  
Search Method ◽  
Early Symptoms ◽  
Forward Chaining

Many pets can be played with, socialize and even live together with humans. Numbers of animal clinics have increased to provide care for pets. This study focuses on Dog as pet. Desease and improper treatment of dog will adversely affect the Dog. In dealing with the problem of Dog disease, Dog owners may experience difficulties due to limited number of clinics and veterinarians, especially in rural areas. As a solution, Artificial Intelligence is used by using expert systems that can help inexperienced medical personnel diagnose early symptoms of Dog disease. The search method used in this research is Forward Chaining and Bayes Theorem method to handle uncertainties that arised. Based on knowledge acquisition, 3 diseases were obtained with 38 simptoms and 60 cases. Based on the tests conducted then obtained the sensitivity value of 80%, the value of accuracy of 88.6% indicates that this expert system is able to diagnose dog diseasesKeywords: Dog, Expert System, Forward Chaining, Bayes Theorem.  

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