The Mechanics of Representing Knowledge

1989 ◽  
Vol 33 (5) ◽  
pp. 361-363 ◽  
Author(s):  
Dick B. Simmons ◽  
Terry D. Escamilla
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Procedural Knowledge ◽  
Clear Understanding ◽  
Full Potential ◽  
Summary Table ◽  
System Building ◽  
Truth Value

This paper describes mechanical knowledge representation schemes found in several popular expert system building tools (ESBTs). In order to realize the full potential of ESBTs, it must be possible to develop a knowledge base in one ESBT and transfer the knowledge into another. Porting a knowledge base across ESBTs requires a clear understanding of the mechanical knowledge representation properties supported by each tool. In the following discussion, properties considered include: canonicity; truth value; plausibility, certainty, and possibility (PCP); temporality; and procedural knowledge. The nature of each property is described along with comments on related knowledge base characteristics. A summary table appears below relating these properties to several popular ESBTs. Overlap found in many of the mechanical knowledge representation properties suggests that automatic knowledge base translation is feasible.

MITIGATING AND MANAGING RISKS IN MOBILE TELECOM PROJECTS

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
M. Mostafa Eid ◽  
Maged Georgy ◽  
Hesham Osman ◽  
Moheeb Ibrahim
Keyword(s):  
Risk Management ◽  
Expert System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Procedural Knowledge ◽  
Project Delivery ◽  
Key Drivers ◽  
Telecom Industry ◽  
Questionnaire Surveys ◽  
Mobile Telecom

Proper risk management is one of the key drivers of successful project delivery. This is more apparent in competitive and fast growing industries such as telecommunication. To better understand the risks faced in construction and deployment of mobile telecom sites (MTS), a research was initiated to: (a) examine risks associated with deploying MTS, and (b) understand the means to mitigate and handle them. The first point was addressed in an earlier publication whilst this paper focuses on the second. Knowledge pertinent to mitigating and handling MTS risks has been acquired via rounds of interviews and questionnaire surveys with a panel of telecom industry experts. Flow charting has been utilized to code the acquired procedural knowledge and provide basis for developing a knowledge base expert system (KBES). The rules technique was selected for knowledge representation. Automation of the KBES is also demonstrated.

Portability of an Expert System Knowledge Base

1989 ◽  
Vol 33 (5) ◽  
pp. 366-369
Author(s):  
Tony H. Haverda ◽  
Peter B. Reitmeyer ◽  
Newton C. Ellis
Keyword(s):  
Expert System ◽  
Problem Solving ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Broad Spectrum ◽  
Operating Environments ◽  
Consistent Model ◽  
Domain Information ◽  
System Knowledge

To ensure the widest possible use of an expert system knowledge base, the knowledge base, in its final form, must be portable to a broad spectrum of user operating environments. Demonstrating that possibility was the objective of the research reported in this paper. Three cognitive issues, knowledge representation, inference mechanisms and problem solving procedures, as they pertain to portability were examined. Structuring the portability question in terms of these cognitive issues, two commercially available expert system shells, EXSYS and TI PC+, were used to ferret out problems and suggest practical solutions. Results determined that it is possible to formulate a consistent model of domain information in a knowledge base which is portable between shells.

A Fuzzy Data Mining Algorithm for Finding Sequential Patterns

2003 ◽  
Vol 11 (02) ◽  
pp. 173-193 ◽  
Author(s):  
Yi-Chung Hu ◽  
Ruey-Shun Chen ◽  
Gwo-Hshiung Tzeng ◽  
Jia-Hourng Shieh
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Effective Algorithm ◽  
Mining Algorithm ◽  
Fuzzy Knowledge

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

Applications of expert system building tools in structural design

1991 ◽  
Vol 18 (3) ◽  
pp. 493-503
Author(s):  
K. M. Sakr ◽  
M. U. Hosain
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Structural Design ◽  
Simple Design ◽  
Inference Mechanism ◽  
System Building ◽  
Knowledge Based ◽  
Real World Applications ◽  
Basic Concepts

This paper summarizes the basic concepts of expert systems and describes some of the applications of three commercially available expert system tools. The function of the various components of the tools is explained using simple design examples. It is concluded that a tool can be employed to develop useful expert systems for real-world applications, provided factual and heuristic material is available for creating a knowledge base. Key words: artificial intelligence, knowledge-based expert systems, knowledge base, inference mechanism, expert system building tools, structural design, applications of expert system building tools.

A TOOL THAT SUPPORTS CONSTRUCTING CHINESE EXPERT SYSTEMS

1987 ◽  
Vol 01 (03n04) ◽  
pp. 393-404
Author(s):  
LI-MIN FU
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
General Purpose ◽  
Data Driven ◽  
Input Output ◽  
Design Decisions ◽  
Rule Based ◽  
System Building ◽  
Control Schemes ◽  
And Control

This paper describes EJAUNDICE, which is designed to be a general-purpose expert system building tool. Considerations behind a number of design decisions for purposes of generality are examined. EJAUNDICE provides several control schemes, including biphasical control with goal-directed reasoning, data-driven processing, and control blocks, and integrates rule-based, frame-based, and logic-based reasoning paradigms in its framework. The issues of knowledge representation and input/output in developing a Chinese expert system are also investigated.

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

Creating a Knowledge Base for a Mivar Expert System for the Diagnosis of Diabetes Mellitus

2021 ◽  
Vol 54 (6) ◽  
pp. 421-424
Author(s):  
H. Kim ◽  
D. A. Chuvikov ◽  
D. V. Aladin ◽  
O. O. Varlamov ◽  
L. E. Adamova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Expert System ◽  
Knowledge Base ◽  
Diagnosis Of Diabetes Mellitus

scholarly journals Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

Sign in / Sign up

Export Citation Format

Share Document