Knowledge Representation and Conversion for Hybrid Expert Systems

1995 ◽  
Author(s):  
Terrence L. Chambers ◽  
Alan R. Parkinson
Keyword(s):  
Knowledge Representation ◽  
Rapid Prototyping ◽  
Expert Systems ◽  
Decision Trees ◽  
Common Knowledge ◽  
Knowledge Engineer ◽  
Knowledge Representations ◽  
Functional Logic ◽  
Decision Tables

Abstract Many different knowledge representations, such as rules and frames, have been proposed for use with engineering expert systems. Every knowledge representation has certain inherent strengths and weaknesses. A knowledge engineer can exploit the advantages, and avoid the pitfalls, of different common knowledge representations if the knowledge can be mapped from one representation to another as needed. This paper derives the mappings between rules, logic diagrams, frames, decision tables and decision trees using the calculus of truth-functional logic. The logical mappings between these representations are illustrated through a simple example, the limitations of the technique are discussed, and the utility of the technique for the rapid-prototyping and validation of engineering expert systems is introduced.

Knowledge Representation and Conversion for Hybrid Expert Systems

1998 ◽  
Vol 120 (3) ◽  
pp. 468-474 ◽  
Author(s):  
T. L. Chambers ◽  
A. R. Parkinson
Keyword(s):  
Knowledge Representation ◽  
Rapid Prototyping ◽  
Expert Systems ◽  
Knowledge Base ◽  
Decision Trees ◽  
Common Knowledge ◽  
Knowledge Engineer ◽  
Knowledge Representations ◽  
Functional Logic ◽  
Decision Tables

Many different knowledge representations, such as rules and frames, have been proposed for use with engineering expert systems. Every knowledge representation has certain inherent strengths and weaknesses. A knowledge engineer can exploit the advantages, and avoid the pitfalls, of different common knowledge representations if the knowledge can be mapped from one representation to another as needed. This paper derives the mappings between rules, logic diagrams, decision tables and decision trees using the calculus of truth-functional logic. The mappings for frames have also been derived by Chambers and Parkinson (1995). The logical mappings between these representations are illustrated through a simple example, the limitations of the technique are discussed, and the utility of the technique for the rapid-prototyping and validation of engineering expert systems is introduced. The technique is then applied to three engineering applications, showing great improvements in the resulting knowledge base.

scholarly journals Knowledge-Based Expert System Development and Validation with Petri Nets

2008 ◽  
Vol 07 (01) ◽  
pp. 37-46 ◽  
Author(s):  
Madjid Tavana
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Petri Nets ◽  
Decision Trees ◽  
System Development ◽  
Semantic Networks ◽  
Knowledge Based ◽  
Medical Diagnostic ◽  
High Level ◽  
Decision Tables

Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

Criteria for Choosing Representation Languages and Control Regimes for Expert Systems

1984 ◽  
Vol 1 (4) ◽  
pp. 2-17 ◽  
Author(s):  
Han Reichgelt ◽  
Frank van Harmelen
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Programming Language ◽  
Knowledge Engineering ◽  
Knowledge Representations ◽  
High Level ◽  
And Control ◽  
Representation Formalism ◽  
Control Regime

AbstractShells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.

Decision trees for rapid prototyping of expert systems

1991 ◽  
Vol 10 (4) ◽  
pp. 110-114 ◽  
Author(s):  
P. Vankeerberghen ◽  
D.L. Massart
Keyword(s):  
Rapid Prototyping ◽  
Expert Systems ◽  
Decision Trees

scholarly journals A Bi-criteria Optimization Model for Adjusting the Decision Tree Parameters

2021 ◽  
Author(s):  
Mohammad Azad ◽  
◽  
Mikhail Moshkov ◽  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Knowledge Representation ◽  
Decision Trees ◽  
Optimization Model ◽  
Optimization Technique ◽  
Trade Off ◽  
Reasonable Number ◽  
Misclassification Rates ◽  
Decision Tables

Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree. Since these two criteria are in conflict, we need to find a decision tree with suitable parameters that can be a trade off between two criteria. Hence, we design two algorithms to build a decision tree with a given threshold of the number of vertices based on the bi-criteria optimization technique. Then, we calculate the local and global misclassification rates for these trees. Our goal is to study the effect of changing the threshold for the bi-criteria optimization of the decision trees. We apply our algorithms to 13 decision tables from UCI Machine Learning Repository and recommend the suitable threshold that can give us more accurate decision trees with a reasonable number of vertices.

Object-Oriented Technology for Expert System Generation

2010 ◽  
pp. 319-336
Author(s):  
Xenia Naidenova
Keyword(s):  
Expert System ◽  
Knowledge Representation ◽  
Rapid Prototyping ◽  
Expert Systems ◽  
Intelligent Systems ◽  
System Development ◽  
Object Oriented ◽  
Effective Algorithm ◽  
Object Oriented Technology ◽  
System Generation

A technology for rapid prototyping expert systems or intelligent systems as a whole is proposed. The main constituents of the technology are the object-oriented model of data and knowledge representation and the mechanism for data-knowledge transformation on the basis of an effective algorithm of inferring all good classification tests. An approach to expert system development by means of this technology is analyzed. The toolkits for expert system generation are described and the application of these tools to the development of a small geological expert system is demonstrated.

scholarly journals Sistem Pakar dalam Akurasi Deteksi Pendarahan pada Kehamilan Menggunakan Metode Forward Chaining

2020 ◽  
pp. 124-129
Author(s):  
Rivo Stephano ◽  
Y Yuhandri
Keyword(s):  
Emergency Room ◽  
Pregnant Women ◽  
Decision Trees ◽  
Medical Records ◽  
Input Data ◽  
Limited Knowledge ◽  
Forward Chaining ◽  
Decision Tables ◽  
In Pregnancy

The occurrence of bleeding in pregnancy is one of the most complications experienced by pregnant women. The limited knowledge possessed by pregnant women about the risks and dangers of bleeding during pregnancy and wrong or late handling when bleeding occurs is one of the factors that cause bad conditions that occur, namely fetuses and pregnant women can die due to bleeding experienced. This study aims to determine the level of accuracy in diagnosing bleeding that occurs in pregnancy by using the Forward Chaining method precisely and accurately. The data processed in this study were as many as 20 data which came from patient medical records and interviews with experts at RSKIA Sukma Bunda Payakumbuh. The processing stages consist of preparing input data, determining decision tables, creating rules, tracking processes, making decision trees, and tracking results. The results of testing this method are that there are 90% of patients who experience bleeding in pregnancy are based on the results of the consultation entered by the user. The results of this test have been able to diagnose bleeding in pregnancy quickly and accurately using the Forward Chaining method and can be recommended to help the doctor in the emergency room to diagnosing bleeding in pregnancy.

scholarly journals Negative Sampling in Knowledge Representation Learning: A Mini-Review

2020 ◽  
Author(s):  
Jing Qian ◽  
Gangmin Li ◽  
Katie Atkinson ◽  
Yong Yue
Keyword(s):  
Knowledge Representation ◽  
Link Prediction ◽  
Representation Learning ◽  
Cluster Sampling ◽  
Continuous Space ◽  
Knowledge Representations ◽  
Space Efficiency ◽  
Low Dimensional ◽  
Fixed Distribution

Knowledge representation learning (KRL) aims at encoding components of a knowledge graph (KG) into a low-dimensional continuous space, which has brought considerable successes in applying deep learning to graph embedding. Most famous KGs contain only positive instances for space efficiency. Typical KRL techniques, especially translational distance-based models, are trained through discriminating positive and negative samples. Thus, negative sampling is unquestionably a non-trivial step in KG embedding. The quality of generated negative samples can directly influence the performance of final knowledge representations in downstream tasks, such as link prediction and triple classification. This review summarizes current negative sampling methods in KRL and we categorize them into three sorts, fixed distribution-based, generative adversarial net (GAN)-based and cluster sampling. Based on this categorization we discuss the most prevalent existing approaches and their characteristics.

Relational mentalizing after any representation

2021 ◽  
Vol 44 ◽  
Author(s):  
Eliane Deschrijver
Keyword(s):  
Knowledge Representation ◽  
Mental State ◽  
Nonhuman Primate ◽  
Social Conflict ◽  
Conflict Monitoring ◽  
Knowledge Representations ◽  
Representation Of Knowledge

Abstract Autistic, developmental, and nonhuman primate populations fail tasks that are thought to involve attributing beliefs, but not those thought to reflect the representation of knowledge. Instead of knowledge representations being more basic than belief representations, relational mentalizing may explain these observations: The tasks referred to as reflecting “belief” representation, but not the “knowledge” representation tasks, are social conflict designs. They involve mental conflict monitoring after another's mental state is represented – with effects that need to be accounted for.

Sign in / Sign up

Export Citation Format

Share Document