• 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.

• 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.

• 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.

• 한국안전학회지
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• 제21권3호
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

• Journal of Information Technology Applications and Management
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• 제12권4호
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• pp.1-12
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• 2005

In general, other conventional researches propose the fuzzy Petri net-based fuzzy reasoning algorithms based on the exhaustive search algorithms. If it can allow the certainty factors representing in the fuzzy production rules to use as the heuristic information, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more effective manner. This paper presents a fuzzy Petri net(FPN) model to represent the fuzzy production rules of a rule-based system. Based on the fuzzy Petri net model, a weighted fuzzy reasoning algorithm is proposed to Perform the fuzzy reasoning automatically, This algorithm is more effective and more intelligent reasoning than other reasoning methods because it can perform fuzzy reasoning using the certainty factors which are provided by domain experts as heuristic information

• 한국전자거래학회지
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• 제8권1호
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• pp.55-70
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• 2003

With the gradual growth of the electronic commerce various forms of shopping malls are constructed, and their searching methods and function are studied many ways. However, the recent outcome is still inadequate to search for goods for the tastes and demands of customers. To construct the shopping mall on the electronic commerce and help customers with purchasing goods, the efficient interface for the customers to contact the shopping malls should be founded and the customers should be able to search the goods they want. Therefore, in this paper, we designed the Intelligent Integration Agent System (IIAS) using the multi-agent formed by the integration agent which integrates the case based reasoning(CBR) and the rule based reasoning(RBR) and the user agent which manages users' profiles. IIAS performs the rule based reasoning on the subject issue first, then provides the unsatisfying search results from the rule-base reasoning to the customers through the user agent, which enables the search of the goods most similar to the ones that meet the tastes and demands of the customers. That is, the accuracy and the speed has been improved by reasoning with the similarity adjustable integration agent which can pick out the goods of customers wants by modifying the weights of properties according to those of the customers.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.1058-1062
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• 1993

A fuzzy reasoning method is proposed for the implementation of control systems based on non-fuzzy microprocessors. The essence of the proposed method is to search the local active miles instead of the global rule base. Thus the reasoning is conveniently performed on a master cell as a fuzzy accelerating kernel, which is transformed from an active fuzzy cell. The interpolative reasoning is simplified via adopting the algebraic product of fulfillment for the conditional connective AND and the weighted average for the rule sentence connective ALSO.

• 한국초등과학교육학회지:초등과학교육
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• 제37권4호
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• pp.372-390
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• 2018

The purpose of this research is to know the scientific reasoning ability of elementary students. In order to find it, 320 elementary students wrote a report about germination of the 700 or 2,000 years old seeds. Their writings were analyzed by scientific writing analysis frameworks, Scientific Reasoning Types and Scientific Reasoning Level Criteria developed by Lim (2018). Minto Pyramid Principles was used to show statements and relations of statements related to scientific reasoning. This paper showed scientific reasoning statements of elementary students about germination of seeds. The characteristics of scientific reasoning of elementary students were as follows. In the process of logical writing by the types of scientific reasoning, many students showed various characteristics and different levels. In the writings based on inductive reasoning, they did not distinguish between common features and differences of cases, and did not derive the rules based on common features and differences of the cases. In the writings based on deductive reasoning, there were cases where the major premise corresponding to the principle or rule was omitted and only the phenomenon was described, or the rule was presented but not connected with the case. In the writings based on abductive reasoning, the ability to selectively use the background knowledge related to the question situation was not sufficient, and borrowing of similar background knowledge, which was commonly used in other situations, was very rare.

• 한국멀티미디어학회논문지
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• 제7권4호
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• pp.559-566
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• 2004

일반적으로 퍼지 생성규칙의 확신도와 규칙에 나타나는 퍼지 명제의 확신도는 0과 1사이의 실수로 표현한다. 만일 퍼지 생성규칙의 확신도와 퍼지 명제의 확신도를 구간값 퍼지 집합으로 표현한다면, 규칙기반시스템이 더 유연한 방법으로 퍼지 추론을 하는 것이 가능하게 된다. 본 논문에서는 퍼지 페트리네트와 이 네트에 기반을 둔 규칙 기반시스템을 위한 구간값 퍼지 집합 후진추론 알고리즘을 제안한다. 규칙 기반시스템에 있는 퍼지 생성규칙은 퍼지 페트리네트로 모형화된다. 여기에서 퍼지 생성규칙에 나타나는 퍼지 명제의 확신도와 규칙의 확신도는 구간값 퍼지 집합으로 표현한다. 여기에서 제안한 알고리즘은 목표노드에서 시작노드까지 후진추론 통로를 찾아낸 후 목표노드의 확신도를 계산한다. 구간값 퍼지 집합 후진추론 알고리즘은 규칙 기반 시스템이 더 유연하고 사람들이 하는 것과 같은 퍼지 후진추론을 가능하게 한다.