Comparison of first order predicate logic, fuzzy logic and non-monotonic logic as knowledge representation methodology

Yang, Kyung Hoon; Olson, David L.; Kim, Jae‐Kyung

doi:10.1016/j.eswa.2004.05.012

Cited by 16 publications

(6 citation statements)

References 18 publications

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“…Yang et al [140] compare first-order predicate logic, fuzzy logic and nonmonotonic logic implemented through negation as failure. The methods were contrasted using a simulation approach in which experiment facts were considered as random numbers.…”

Section: Comparison Of Non-monotonic Formalismsmentioning

confidence: 99%

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Rizzo

Longo

2022

SSRN Journal

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Section: Comparison Of Non-monotonic Formalismsmentioning

confidence: 99%

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Rizzo

Longo

2022

SSRN Journal

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“…Dutilh Novaes & Veluwenkamp (2017) make an empirical test of the accuracy of two formal non-monotonic reasoning models: preferential logic and screened belief revision. Yang et al (2004) compare first order predicate logic, fuzzy logic and non-monotonic logic implemented through negation as failure. Despite highlighting interesting connections among these formalisms, the focus of the studies is usually theoretical or limited by a narrow scope.…”

Section: Non-monotonic Reasoningmentioning

confidence: 99%

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

Rizzo

Longo

2020

Expert Systems with Applications

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Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined

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“…Knowledge representation is important because accuracy, inference and knowledge retrieval all depend on its accuracy. Therefore, a good knowledge representation should have the capability to store and retrieve knowledge accurately and quickly (Yang et al, 2004). Association rule mining as a method of knowledge discovery is explained below.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Recruitment Process in Human Resources Management

Bal

2022

International Journal of Decision Support System Technology

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Discovering and using valuable and meaningful data which is hidden in large databases can have strategic importance in the managerial decision making process for organizations to gain competitive advantage. With the increasing data flow, it has become more difficult for organizations to store this data and gain useful knowledge to manage their business operations and functions. Knowledge discovery process that is based on data mining methods has widely been used in business operations and management functions.This paper investigates formal concept analysis which is a powerful tool in knowledge representation and discovery and explains association rule mining based-on formal concept analysis. An experimental study is given for employee selection function ofHRM by using formal concept analysis method to model the qualifications of candidates which are needed for the job position. The qualifications of the candidates are modelled with concept lattices and the qualifications of the candidates are matched with the ones determined in the job specification.

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Comparison of first order predicate logic, fuzzy logic and non-monotonic logic as knowledge representation methodology

Cited by 16 publications

References 18 publications

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

A Novel Approach for the Recruitment Process in Human Resources Management

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