Journal of Intelligence and Information Systems,
Vol. 6, No. 1, June 2000
Automatic Generation of Web-based Expert Systems
Yong Uk Song, and Jae Kyu Lee
Vol. 6, No. 1, Page: 1 ~ 16
This paper analyzes the approaches of Web-based expert systems by comparing their pros and cons. and proposes a methodology of implementing the Web-based backward inference engines with reduced burden to Web servers. There are several alternatives to implement expert systems under the WWW environment : CGI, Web servers embedding inference engines external viewers Java Applets and HTML. Each of the alternatives have advantages and disadvantages of each own in terms of development and deployment testing scalability portability maintenance and mass service. Especially inference engines implemented using HTML possess relatively large number of advantages compared with those implemented using other techniques. This paper explains the methodology to present rules and variables for backward inference by HTML and JavaScript and suggests a framework for design and development of HTML-based Expert System. A methodology to convert a traditional rule base to an Experts Diagram and then generate a new HTML-based Expert System from the Experts Diagram is also addressed.
Virtual Reality Internet Shopping Mall By Using Avatar and Intelligent Shopping Agent -Emphasis on Web Decision Support System-
Kun Chang Lee, Nam Ho Chung, and Nam Ho Lee
Vol. 6, No. 1, Page: 17 ~ 34
Meta Knowledge for Effective Model Management in Web-based System
Chulsoo Kim
Vol. 6, No. 1, Page: 35 ~ 50
Keywords : web-based system, model management, system agent
Diverse requirements of users on web-based model management force a system agent to develop user-adaptive building a model in reality and providing an adequate solution method of the model. The relationship between models is important knowledge for the agent to effectively build a new model to adaptively adjust an existing model under a problem and to efficiently connect the new model into an adequate solution method. Since the generating process of the inter-model relationship is more difficult than the building a new model however the process mostly depends on the knowledge of operation research experts. Without the adequate scheme of the inter-model relationship the burden of the management for the agent increases rapidly and the quality of the services may worsen. This study shows that meta-knowledge generated from relationship between models is important for the user to build a model in reality and to acquire the solver appropriate to the model. The relationship that consists of common and exclusive objects between models can be represented by frames. The system under development to implement the idea includes user-adaptive ability which identifies a model through forward chaining method and searches the solver appropriate to the model by using the meta knowledge. We illustrate the meta knowledge with an applied delivery system in supply chain management.
A Study on Integrated Intelligent SCADA System for Industrial Facilities Management
Sung-Yul Lee, Chan-Bum Yang, Suk-Hoon Yang, Young-Tack Park, and Sung-Young Lee
Vol. 6, No. 1, Page: 51 ~ 64
Keywords : Integrated Diagnostic system, Facilities Management System, Integrated Intelligent SCADA System
Comparing object images using fuzzy-logic induced Hausdorff Distance
Hwan-Il Kang, and Young-Gi Song
Vol. 6, No. 1, Page: 65 ~ 72
Keywords : Hausdorff distance, Image matching, COA
In this paper we propose the new binary image matching algorithm called the Fuzzy logic induced Hausdorff Distance(FHD) for finding the maximally matched image with the query image. The membership histogram is obtained by normalizing the cardinality of the subset with the corresponding radius after obtaining the distribution of the minimum distance computed by the Hausdroff distance between two binary images. in the proposed algorithm, The fuzzy influence method Center of Gravity(COG) is applied to calculate the best matching candidate in the membership function described above. The proposed algorithm shows the excellent results for the face image recognition when the noise is added to the query image as well as for the character recognition.
Data Mining for Knowledge Management in a Health Insurance Domain
Young Moon Chae, Seung Hee Ho, Kyoung Won Cho, Dong Ha Lee, and Sun Ha Ji
Vol. 6, No. 1, Page: 73 ~ 82
Keywords : Knowledge Management, Data Mining, Logistic regression, Hypertension, Decision Support System
This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.
Diagnosis and Scheduling Agent Systems for Collaborative Learning
Sun-Gwan Han, Se-Hyoung Kim, and Geun-Sik Jo
Vol. 6, No. 1, Page: 83 ~ 96
Keywords : Intelligent Tutoring System, Collaborative learning, Dynamic Scheduling Constraint Satisfaction Problem
Multiple Case-based Reasoning Systems using Clustering Technique
Jae Sik Lee, and Ja-Young Kang
Vol. 6, No. 1, Page: 97 ~ 112
Keywords : Multiple Case-based Reasoning System, Clustering, Malfunction Diagnosis
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.
A Systolic Parallel Simulation System for Dynamic Traffic Assignment : SPSS-DTA
Kwangho Park, and Wonkyu Kim
Vol. 6, No. 1, Page: 113 ~ 128
Keywords : Dynamic Traffic Assignment, Simulation, Parallel Processing, Systolic Parallel Processing, Agent-Based Modeling
This paper presents a first year report of an ongoing multi-year project to develop a systolic parallel simulation system for dynamic traffic assignment. The fundamental approach to the simulation is systolic parallel processing based on autonomous agent modeling. Agents continuously act on their own initiatives and access to database to get the status of the simulation world. Various agents are defined in order to populate the simulation world. In particular existing modls and algorithm were incorporated in designing the behavior of relevant agents such as car-following model headway distribution Frank-Wolf algorithm and so on. Simulation is based on predetermined routes between centroids that are computed off-line by a conventional optimal path-finding algorithm. Iterating the cycles of optimization-then-simulation the proposed system will provide a realistic and valuable traffic assignment. Gangnum-Gu district in Seoul is selected for the target are for the modeling. It is expected that realtime traffic assignment services can be provided on the internet within 3 years.

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