• 한국지능시스템학회논문지
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• 제21권2호
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• pp.165-170
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• 2011

규칙베이스 추론과 사례베이스 추론의 협조에 의해 보다 유연한 추론을 위한 효율적인 방법의 실현이 기대된다. 본 논문에서는 MVL 오토마타 모델을 적용하여 규칙베이스와 사례 베이스의 통합 추론모델과 이에 따른 불확실성 처리 방법을 제안한다.

• 한국정보처리학회논문지
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• 제5권1호
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• pp.103-110
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• 1998

기존의 규칙베이스 추론(Rule-Based REasoning : RBR)과 사례베이스 추론 (Case-Base : CB)가 통합되어 추론되고 있지만, 많은 수의 규칙(Rule)과 사례(Case)에 의해 추론 시간이 많이 걸리는 단점이 있다. 본 논문에서는 이런 단점을 해결하기 위하여, 다중 의미 또는 불확실한 지식을 쉽게 표현할 수 있는 라프집합 (Rough Set)을 이용하여 RB와 CB를 간략화한 새로운 추론 방법을 제안한다. 라프집합의 식별(classification)과 근사(aprroximation)개념을 이용하여, RB와 CB를 통치 클래스(equivalence class)로 분류하여 각각을 각략화하고, 간략화된 RB와 CB를 이용하여 통합 추론하여, 상호 보완적인 역할에 의해 결정 해를 얻고자 하는 것이다.

• International Journal of Safety
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• 제4권2호
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• International Journal of Safety
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• 제6권2호
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• pp.27-32
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• 2007

For last several decades with the achievement of fast economic development, the electrical fires occupies over 30 percent of total fire incidents almost every year in Korea and not decreased in spite of much times and efforts. Electrical fire cause diagnostics are to confirm a cause for the fire by examination of fire scene. Cause diagnosis methods haven't been systematized yet, because of limits for available information, investigator's biased knowledge, etc. Therefore, in order to assist the investigators and to find out the exact causes of electrical fires, required is research for an electrical fire cause diagnosis system using DB, computer programming and some mathematical tools. The electrical fire cause diagnosis system has two functions of DB and electrical fire cause diagnosis. The cause diagnosis is conducted by a case-based reasoning on a case base and rule-based reasoning on a rule base. For the diagnosis with high reliability, a mixed reasoning approach of a case-based reasoning and fuzzy rule-based reasoning has been adopted. The electrical fire cause diagnosis system proposes the electrical fire causes inferred from the diagnosis processes, and possibility of the causes as well.

• 동의생리병리학회지
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• 제23권4호
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• 산업경영시스템학회지
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• 제21권46호
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• pp.181-196
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• 1998

The aim of this research is the development of a CAPP system which can use the old experience of process planning to generate a process plan for a new part and learn from its own experience using the concept of stratified case-based reasoning(CBR). A process plan is determined through the hierarchical process planning procedure that is based on the hierarchical feature structure of a part. Each part and case have their own multiple abstractions that are determined by the feature structure of the part. Retrieving the case in stratified case-based process planning is accomplished by retrieving the abstraction that is most similar to the input part abstraction in each abstraction level of the case-base. A new process plan is made by the adaptation that translates the old case's process plan into the process plan of a new part. Operations, machines and tools, setups and operation sequence in each setup are determined in the adaptation of abstraction using some algorithms and the reasoning based on knowledge-base. By saving a new part and its process plan as a case, the system can use this new case in the future to generate a process plan of a similar part. That is, the system can learn its own experience of process planning. A new case is stored by adding the new abstractions that are required to save as the new abstraction to the existing abstractions in the case-base.

• Journal of Ship and Ocean Technology
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• 제5권3호
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• pp.27-35
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• 2001

In this research, hybrid method with case-based reasoning and rule-based reasoning is applied. Using case-based reasoning, design experts'experience and know-how are effectively represented in order to obtain a proper configuration of midship section in the initial ship design stage. Since there is not sufficient domain knowledge available to us, traditional case-adaptation algorithms cannot be applied to our problem, i.e., creating the configuration of midship section. Thus, new case-adaptation algorithms not requiring any domain knowledge are developed antral applied to our problem. Using the knowledge representation of DnV rules, rule-based reasoning can perform deductive inference in order to obtain the scantling of midship section efficiently. The results from the case-based reasoning and the rule-based reasoning are examined by comparing the results with various conventional methods. And the reasonability of our results is verified by comparing the results wish actual values from parent ship.

• 지능정보연구
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• 제6권1호
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• 정보처리학회논문지D
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• 제11D권5호
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• pp.1189-1196
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• 2004

e쇼핑몰 경영자들은 고객들의 다양한 제품 구매 욕구를 충족시키기 위한 효율적 시스템에 많은 관심을 가지고 있다. 인터넷 쇼핑몰 운영에 있어 고객들의 개인적 구매 특성 및 취향을 파악하여 고객들을 효과적으로 관리하는데 많은 어려움이 있다. 상품 추천의 과정이 기획된 소수의 특정 상품을 고객의 유형 및 특성들의 고려 없이 공급자 중심으로 이루어져 고객관리의 문제점으로 지적되고 있다. 본 연구에서는 고객위주의 추천을 위해 규칙기반추론(Rule-Based Reasoning, RBR)과 사례기반추론(Case-Based Reasoning, CBR)을 하여 고객의 취향 및 구매 특성에 따른 추천방법을 제시한다. 기존의 제품 판매정보와 고객정보를 이용해 생성한 규칙베이스와 사례베이스의 고객특성과 입력된 고객특성의 유사도를 평가해서 고객의 취향에 따라 추천하도록 한다. 생성된 규칙과 사례기반의 추론으로 기존의 정보를 효과적으로 사용하고 또한 고객 및 시장 상황의 변화를 인식하고 지속적인 학습을 수행하여 지능적 추천이 이루어진다.

• 지능정보연구
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• 제3권2호
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.