• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.49-54
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• 2011

Case-based reasoning is an efficient method to find solutions for new problems by using past cases after appropriate changes. It is widely used in everyday life because it resembles the way human acts. In this paper, we propose a military system that generates the most appropriate tactics for CGF (Computer Generated Forces) by utilizing past practices. It indeed applies case-based reasoning at the process of armed conflict. When the CGF squad on a mission, they will be given an action plan to reach the final goal. In the process of executing, tactics for specific action should be organized such as attacks, ambushes, and tactical moves. By using the proposed method, tactics were generated by case-based reasoning. The proposed system successfully receives input through each command and control agent, measures the degree of similarity with the case in case DB, selects the most similar case, modifies, uses, and then stores it for next time.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.42-50
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm considering marine feature because existing vessel USN system is simply monitoring obtained data from vessel USN. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for fire and demage of digital marine vessel. We used K-Nearest Neighbor algorithm for recommend best similar case and input 3.000 EA by case for fire and demage context case base. As a result, we obtained about 82.5% average accuracy for fire case and about 80.1% average accuracy for demage case. We implemented digital marine vessel monitoring system using inference result.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.1-13
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• 2002

Estimating the Value-at-Risk (VaR) of a non-listed or newly listed company in stock market is impossible due to lack of stock exchange data. This study employes Case-Based Reasoning (CBR) for estimating VaR's of those companies. CBR enables us to identify and select existing companies that have similar financial and non-financial characteristics to the unlisted target company. The VaR's of those selected companies can give estimates of VaR for the target company. We developed a system called VAS-CBR and showed how well the system estimates the VaR's of unlisted companies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.81-86
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• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.

• Journal of Korean Association for Spatial Structures
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• v.7 no.6
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• pp.75-82
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• 2007

In this study, a design-supporting system, which is intended to assist engineers in the schematic phase of the structural design, is developed using a case database that contains design information of tall buildings in Korea. A case-based reasoning method utilizing the case database is proposed. The inductive retrieval module for selecting structural system, in the initial stage, from the design information of case database for 47 tall buildings is presented. Also, the nearest-neighbor retrieval method for selecting similar design cases is introduced.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.100-103
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• 2002

사례 기반 추론(case-based reasoning)은 과거에 유사하게 수행된 적이 있는 사레를 유추하고, 유추된 사례의 해를 이용하여 현재의 문계를 해결하는 기법으로서 규칙 기반 추론과 함께 여러 분야에 이용되고 있다. 하지만 사례기반 추론시 사레베이스로부터의 유사성에 근거한 검색을 해야 하므로 사례베이스의 크기가 증가하게 되면 검색시간이 길어지게 되거나 적절하지 못한 사레가 조회될 수 있다 특히 사레베이스 내의 모든 사례에 대하여 유사도를 계산하게 되기 때문에 수행속도가 현저히 저하되는 문제점을 지니고 있다. 본 논문에서는 규칙 및 퍼지 클러스터링에 의한 사레기반추론을 이용한 E-FFIS(Enhanced-Fire Fighting Intelligent System)를 제안한다. 제안하는 시스템은 기존의 H-FFIS(Hybrid-Fire fighting Intelligent System)와 비교해 보았을 때 수행시간을 감소시키면서 정확성을 높이게 되었다.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1207-1213
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• 2006

This paper shows that case-based reasoning (CBR), an artificial intelligence technique, is a quite efficient tool in monitoring financial market against its possible collapse. For this purpose, daily financial condition indicator (DFCI) monitoring financial market is built on CBR and its performance is compared to DFCI on neural network. This study is empirically done for the Korean financial market.