A LOGICAL ANALYSIS OF RULE INCONSISTENCY

2011 ◽  
Vol 05 (03) ◽  
pp. 271-280 ◽  
Author(s):  
PHILIPPE BESNARD
Keyword(s):  
Natural Deduction ◽  
Logical Analysis ◽  
Production Rules ◽  
Rule Based ◽  
Material Implication ◽  
Classical Sense ◽  
Rule Based System ◽  
New Information ◽  
Rule Consistency ◽  
Rule Based Systems

Representing knowledge in a rule-based system takes place by means of "if…then…" statements. These are called production rules for the reason that new information is produced when the rule fires. The logic attached to rule-based systems is taken to be classical inasmuch as "if…then…" is encoded by material implication. However, it appears that the notion of triggering "if…then…" amounts to different logical definitions. The paper investigates the matter, with an emphasis upon consistency because reading "if… then…" statements as rules calls for a notion of rule consistency that does not conform with consistency in the classical sense. Natural deduction is used to explore entailment and equivalence among various formulations and properties.

Acquiring Objective Functions in Distributed Rule-Based Systems from Examples

1996 ◽  
Vol 8 (5) ◽  
pp. 454-458
Author(s):  
Kenichi Matsuura ◽  
◽  
Yukinori Kakazu
Keyword(s):  
Problem Solving ◽  
Objective Function ◽  
Traveling Salesman ◽  
Rule Generation ◽  
Objective Functions ◽  
Rule Based ◽  
Rule Based System ◽  
System A ◽  
Distributed Problem Solving ◽  
Rule Based Systems

There are some great features in distributed problem solving systems, such as fault tolerance, robustness and so on. This system performs problem solving with search depending on an objective function. Distributed rulebased problem systems are considered to be of the same type. That is to say, the set of rules and the objective function exist separately within the system. However, in distributed rule-based systems, a set of rules should hold the objective function. The system should have a set of rules only, and the objective function should exist within that set of rules. In this paper, our objective is to acquire the objective function of a distributed rule-based system. A rule generation mechanism analyzes some given examples and acquires strategies for problem solving to a set of rules. In this way, the set of rules of the examples class in the domain represents the objective function of that class in the domain. Therefore, a solution using those rules keeps the same features as the examples if the problem belongs to the examples class that generates the set of rules. The system implemented by this theory has been applied to the domain of traveling salesman problem. This system has generated a set of rules that has held the objective function of its domain.

scholarly journals Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

2020 ◽  
Vol 8 (5) ◽  
pp. 1335-1340
Keyword(s):  
Fuzzy Rule ◽  
Vital Role ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Human Decision ◽  
Fuzzy Partitions ◽  
Accuracy Assessments ◽  
Improved Accuracy ◽  
Rule Based Systems

Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

USING RULE STRUCTURE TO EVALUATE THE COMPLETENESS OF RULE-BASED SYSTEM TESTING: A CASE STUDY

2010 ◽  
Vol 20 (07) ◽  
pp. 975-986 ◽  
Author(s):  
STEPHEN C. MEDDERS ◽  
EDWARD B. ALLEN ◽  
EDWARD A. LUKE
Keyword(s):  
Test Suite ◽  
Application Framework ◽  
Test Coverage ◽  
Prior Work ◽  
Rule Based ◽  
Coverage Metrics ◽  
Rule Based System ◽  
Regression Test ◽  
Rule Based Systems

Rule-based systems are typically tested using a set of inputs which will produce known outputs. However, one does not know how thoroughly the software has been exercised. Traditional test-coverage metrics do not account for the dynamic data-driven flow of control in rule-based systems. Our literature review found that there has been little prior work on coverage metrics for rule-based systems. This paper proposes test-coverage metrics for rule-based systems derived from metrics defined by prior work, and presents an industrial scale case study. We conducted a case study to evaluate the practicality and usefulness of the proposed metrics. The case study applied the metrics to a system for computational fluid-dynamics models based on a rule-based application framework. These models were tested using a regression-test suite. The data-flow structure built by the application framework, along with the regression-test suite, provided case-study data. The test suite was evaluated against three kinds of coverage. The measurements indicated that complete coverage was not achieved, even at the lowest level definition. Lists of rules not covered provided insight into how to improve the test suite. The case study illustrated that structural coverage measures can be utilized to measure the completeness of rule-based system testing.

FUZZY GRAMMAR FOR HANDLING FUZZY ALGORITHMS

1999 ◽  
Vol 07 (03) ◽  
pp. 277-286
Author(s):  
J. L. CASTRO ◽  
M. DELGADO ◽  
C. J. MANTAS
Keyword(s):  
Fuzzy Rule ◽  
Formal Languages ◽  
Production Rules ◽  
Learning Capability ◽  
Rule Based ◽  
Fuzzy Algorithm ◽  
Rule Based System ◽  
Fuzzy Algorithms

This paper presents a new procedure for handling fuzzy algorithm. It consists of transforming the steps of a fuzzy algorithm into production rules of a fuzzy grammar, executing it using the dynamic of this grammar and adjusting its execution by means of the learning capability of the fuzzy formal languages. With this method, some flexible criterial about the goodness of the execution of a fuzzy algorithm are only necessary for adjusting it. Finally, this procedure is applied for executing and adjusting a fuzzy rule-based system.

scholarly journals Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules

2019 ◽  
Vol 9 (22) ◽  
pp. 4962
Author(s):  
Chao Ou-Yang ◽  
Chandrawati Putri Wulandari ◽  
Mohammad Iqbal ◽  
Han-Cheng Wang ◽  
Chiehfeng Chen
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Clinical Decision Making ◽  
Computational Time ◽  
Production Rules ◽  
Health Records ◽  
Rule Based ◽  
Reasoning Process ◽  
Rule Bases ◽  
Rule Based Systems

Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and affects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time.

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