• Korean Management Science Review
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• v.21 no.3
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• pp.39-54
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• 2004

This paper presents a multi-agent system for intelligent recommendation of travel plans to users. The goal of the system is to provide alternative and preferable travel plans to users when the availability of tickets is low such as in vacations, holidays, weekends, or peak seasons. The multiple agents in the system search for available alternatives for a target travel in collaboration with other agents and recommend best alternatives by analyzing them using a multi-criteria decision-making model. A prototype online travel support system was constructed and a simulation experiment was performed for evaluation and comparison with different travel planning strategies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.331-337
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• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.170-174
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• 1997

This paper studies a method for making more efficient classification rules in the ID3 using the rough set theory. Decision tree technique of the ID3 always uses all the attributes in a table of examples for making a new decision tree, but rough set technique can in advance eleminate dispensable attributes. And the former generates only one type of classification rules, but the latter generates all the possibles types of them. The rules generated by the rough set technique are the simplist from as proved by the rough set theory. Therefore, ID3, applying the rough set technique, can reduct the size of the table of examples, generate the simplist form of the classification rules, and also implement an effectie classification system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.261-264
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• 2004

컴퓨터 전략 시뮬레이션 게임 설계에서는 Master ADU(Artificial Decision Unit)의 전략 수립을 위한 방법으로 다양한 기법들이 연구되고 있다. 특히 한정된 자원 하에서 게임을 사실적이고 지적인 기능을 구현하기 위해 치팅(Cheating)을 활용하거나 간단한 인공지능 기법이 적용되고 있다. 하지만 이 기법들은 사용자 적응성 및 전략 수립의 단순성을 야기하는 단점을 가지고 있다. 본 연구에서는 전략 시뮬레이션 게임의 전략 수립 에이전트인 Master ADU(Artificial Decision Unit)를 위하여 셀룰라 오토마타의 초기 규칙 생성에 유전자 알고리즘의 교배 및 돌연변이, 적합도 평가를 거친 유전자 형을 적용한 혼합형 전략 수립 기법을 제안한다 이 기법은 ADU가 적합한 유전자 형을 생산 및 선택하여 사용자에 대해 적극적으로 학습할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.666-674
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• 2001

Classical methods to find a minimal set of rules based on the rough set theory are known to be ineffective in dealing with new instances added to the universe. This paper introduces an inductive learning algorithm for incrementally retrieving a minimal set of rules from a given decision table. Then, the algorithm is validated via simulations with two sets of data, in comparison with a classical non-incremental algorithm. The simulation results show that the proposed algorithm is effective in dealing with new instances, especially in practical use.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.3-15
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• 1997

In order to apply fuzzy logic, two major tasks need to be performed: the derivation of fuzzy rules and the determination of membership functions. These tasks are often difficult and time-consuming. This paper presents an algorithmic method for generating membership functions and fuzzy rules applicable to decision-making problems; the method includes an entropy minimization for clustering analog samples. Membership functions are derived by partitioning the variables into desired number of fuzzy terms, and fuzzy rules are obtained using minimum entropy clustering. In the mle derivation process, rule weights are also calculated. Inference and defuzzification for classification problems are also discussed.

• Asia pacific journal of information systems
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• v.2 no.1
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• pp.35-56
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• 1992

This paper proposes a synergism of neural networks (NN) and knowledge-based decision support system (KBDSS) to effectively design an intelligent strategic planning system. Since conventional KBDSS becomes inoperative partially or totally when problem deviates slightly from the expected problem-domain, a new DSS concept is needed for designing an effective strategic planning system, where strategic planning environment is usually turbulent and consistently changing. In line with this idea, this paper developes a NN-based DSS, named ConDSS, incorporating the generalization property of NN into its knowledge base. The proposed ConDSS was extensively operated in an experimentally designed environment with three models: BCG matrix, Growth/Gain matrix, and GE matrix. The results proved very promising when encountered with unforeseen situations in comparisons with conventional KBDSS.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.54-57
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• 2016

In this paper, the method for an effective and intelligent route decision of the automatic ground vehicle (AGV) using a 2D spatial map of the stereo camera system is proposed. The depth information and disparity map are detected in the inputting images of a parallel stereo camera. The distance between the automatic moving robot and the obstacle detected and the 2D spatial map obtained from the location coordinates, and then the relative distance between the obstacle and the other objects obtained from them. The AGV moves automatically by effective and intelligent route decision using the obtained 2D spatial map. From some experiments on robot driving with 480 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the distance between the objects is found to be very low value of 1.57% on average, respectably.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.165-168
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• 1999

The support or automation of various kinds of intelligent work is urged at large, integrated control centers. Given this demand, a decision making system for wash timing of polluted insulators, applying the Bayesian rule theory, has been developed in order to support maintenance work in the power system. The results of this system application revealed that exact wash timing of the insulators could be determined automatically, equivalent in precision to judgement by skilled operators, thus contributing to further work efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.