Constructing Modular Hybrid Rule Bases for Expert Systems

Hatzilygeroudis, Ioannis; Prentzas, Jim

doi:10.1142/s021821300100043x

Cited by 27 publications

(28 citation statements)

References 3 publications

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“…Neurules can be constructed either from symbolic rules thus exploiting existing symbolic rule bases [4] or from empirical data (i.e. training patterns) [5]. In each process, an adaline unit is initially assigned to each intermediate and final conclusion and the corresponding training set is determined.…”

Section: The Neurule-based Expert Systemmentioning

confidence: 99%

“…These neurules are produced off-line from available empirical data concerning past loan cases (see Section 5). The construction process is performed by the neurule construction mechanism (NCM) [5]. …”

Section: The Neurule-based Expert Systemmentioning

confidence: 99%

“…Neurules exhibit advantages compared to pure symbolic rules such as, improved inference performance [4], ability to reach conclusions from unknown inputs and construct knowledge bases from alternative sources (i.e. symbolic rules or empirical data) [4], [5]. Neurules were produced from available cases (past loan applications).…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy and Neuro-Symbolic Approaches to Assessment of Bank Loan Applicants

Hatzilygeroudis

Prentzas

2011

IFIP Advances in Information and Communication Technology

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Abstract. In this paper, we present the design, implementation and evaluation of intelligent methods that assess bank loan applications. Assessment concerns the ability/possibility of satisfactorily dealing with loan demands. Different loan programs from different banks may be proposed according to the applicant's characteristics. For each loan program, corresponding attributes (e.g. interest, amount of money that can be loaned) are also calculated. For these tasks, two separate intelligent systems have been developed and evaluated: a fuzzy expert system and a neuro-symbolic expert system. The former employs fuzzy rules based on knowledge elicited from experts. The latter is based on neurules, a type of neuro-symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. Neurules were produced from available patterns. Evaluation showed that performance of both systems is close although their knowledge bases were derived from different types of source knowledge.

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Section: The Neurule-based Expert Systemmentioning

confidence: 99%

Section: The Neurule-based Expert Systemmentioning

confidence: 99%

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Fuzzy and Neuro-Symbolic Approaches to Assessment of Bank Loan Applicants

Hatzilygeroudis

Prentzas

2011

IFIP Advances in Information and Communication Technology

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“…The expert system's knowledge representation formalism is based on neurules, a type of hybrid rules integrating symbolic rules with neurocomputing. The attractive feature of neurules is that they improve the performance of symbolic rules [6] and simultaneously retain their naturalness and modularity [7] in contrast to other hybrid approaches.…”

Section: Expert Systemmentioning

confidence: 99%

“…Based on this information, it constructs lesson plans and selects the appropriate course units for teaching each individual user. The functionality of the system is controlled by an expert system based on neurules, a type of hybrid rules integrating symbolic rules with neurocomputing [6], [7]. Fig.…”

mentioning

confidence: 99%

A Web-Based Intelligent Tutoring System Teaching Nursing Students Fundamental Aspects of Biomedical Technology

Koutsojannis¹,

Prentzas²,

Hatzilygeroudis³

2001

Self Cite

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Abstract-In this paper, we present the architecture of a Web-based Intelligent Tutoring System (ITS) for distant education of nursing students in fundamental aspects of the most common medical equipment. It offers course units covering the needs of users with different knowledge levels and characteristics. It tailors the presentation of the educational material to the users' diverse needs by using AI techniques to specify each user's model as well as to make pedagogical decisions. This is achieved via an expert system that uses a hybrid knowledge representation formalism integrating symbolic rules with neurocomputing. Keywords -Intelligent Tutoring Systems, health care technology I. INTRODUCTIONThe introduction of new technologies in health care, can be seen as improved diagnosis and treatment as well as increased efficiency of the services involved. At the same time, however, the implementation of these new improved technologies, introduces concerns about trained personnel and imposes new demands for tools and methods for their overall management. The support services, for biomedical technology, address a variety of technical and administrative issues, concerning the safe and efficient operation of medical equipment over the period of its intended use. Health care technology is our first line of defence against any disease. Health care professionals such as nurses have to be familiar with the most common medical equipment used in health care units. Educational programs already include courses with the fundamental aspects of the proper care and safety of medical instruments [12] In this paper, we present the architecture of a Web-based ITS for teaching the use and safety aspects of health care technology. The system is addressed to nursing students or health professionals. It includes course units covering the needs of users with different knowledge levels and characteristics. The system models the users' knowledge state and skills. Based on this information, it constructs lesson plans and selects the appropriate course units for teaching each individual user. The functionality of the system is controlled by an expert system based on neurules, a type of hybrid rules integrating symbolic rules with neurocomputing [6], [7]. Fig. 1 depicts the basic architecture of the ITS. It consists of the following components: II. SYSTEM DESCRIPTION• the domain knowledge, containing the structure of the domain and the educational content. The educational content refers to the following main chapters: magnetic resonance, computed tomography, nuclear medicine, electro-medicine, ultrasound, anesthesia systems, radiographic systems, medical informatics, lasers and intensive care systems. Each chapter concerns specific equipment. For each piece of equipment primary learning items involve its structure, its manipulation and internationally recognized safety standards.• the user modeling component, which records information concerning the user, • the pedagogical model, which encompasses knowledge regarding various pedagogical decision...

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Multi-inference with Multi-neurules

Hatzilygeroudis

Prentzas

2002

Methods and Applications of Artificial Intelligence

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Constructing Modular Hybrid Rule Bases for Expert Systems

Cited by 27 publications

References 3 publications

Fuzzy and Neuro-Symbolic Approaches to Assessment of Bank Loan Applicants

Fuzzy and Neuro-Symbolic Approaches to Assessment of Bank Loan Applicants

A Web-Based Intelligent Tutoring System Teaching Nursing Students Fundamental Aspects of Biomedical Technology

Multi-inference with Multi-neurules

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