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What is Rule-Based Reasoning

Chapter 17
Handbook of Research on Promoting Logistics and Supply Chain Resilience Through Digital Transformation
In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g., IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Published in Chapter:
Supply Chain Resilience Management Using a Semantically-Enhanced Web Service Portal
Kamalendu Pal (City, University of London, UK)
Copyright: © 2023 |Pages: 22
DOI: 10.4018/978-1-6684-5882-2.ch017
Abstract
This chapter aims to investigate the research initiatives in supply chain resilience-related issues and presents the advantages of a semantically enhanced web service portal to mitigate knowledge and experience management solutions. The knowledge and experience gained in the past help identify and manage resilience in supply chain operations dependency. Such understanding and knowledge reside in the operation manager's mind and are rarely available in a reusable form. Hence, a software system with a case based on prior disturbance mitigating actions can assist managers in risk management of industrial supply chain operations. This chapter presents the features of an ontology-based web-portal framework, SCRMA (Supply Chain Resilience Management Architecture), for risk management in industrial supply chain projects. The SCRMA framework is a semantic service composition using case-based reasoning (CBR), rule-based reasoning (RBR), and an ontology-based concept similarity assessment module. Finally, a business scenario demonstrates some of the system's functionalities.
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Chapter 6
Semantically Enhanced Web Service for Global Supply Chain Disruption Management
In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g. IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Published in Chapter: Semantically Enhanced Web Service for Global Supply Chain Disruption Management; From: Digitalization of Decentralized Supply Chains During Global Crises
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Chapter 9
Framework for Reusable Test Case Generation in Software Systems Testing
In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise (i.e. software testing) are represented in the forms of plausible rules (e.g. IF <precondition(s)> THEN <conclusion(s)>. For example, an instance of a rule in common law: IF {(Jo has a driving license) AND (Jo is drunk) AND (Jo is stopped by police)} THEN {(Jo’s driving license will be revoked by the transport authority)}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Published in Chapter: Framework for Reusable Test Case Generation in Software Systems Testing; From: Software Engineering for Agile Application Development
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Chapter 10
Decision Support in the Elderly Healthcare: Combing Short- and Long-Term Analysis Aspects
The representation of an expert task as a rule. As a rule it is meant of a programmatic structure which contains an “if” and a “then” component.
Published in Chapter: Decision Support in the Elderly Healthcare: Combing Short- and Long-Term Analysis Aspects; From: Handbook of Research on Innovations in the Diagnosis and Treatment of Dementia
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Chapter 161
Ambient-Intelligent Decision Support System (Am-IDSS) for Smart Manufacturing
The process of learning to solve new problems based on the research and the application of AI.
Published in Chapter: Ambient-Intelligent Decision Support System (Am-IDSS) for Smart Manufacturing; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 41
Web Service in Knowledge Management for Global Software Development
In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g., IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Published in Chapter: Web Service in Knowledge Management for Global Software Development; From: Encyclopedia of Data Science and Machine Learning
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Chapter 7
Evaluation of a Scenario-Based Socratic Style of Teaching and Learning Practice
In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g., IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Published in Chapter: Evaluation of a Scenario-Based Socratic Style of Teaching and Learning Practice; From: Enhancing Teaching and Learning With Socratic Educational Strategies: Emerging Research and Opportunities
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