• Asia pacific journal of information systems
• /
• v.12 no.2
• /
• pp.179-195
• /
• 2002

In this research, we developed an efficient Hangul document classification system for text mining. We mean 'efficient' by maintaining an acceptable classification performance while taking shorter computing time. In our system, given a query document, k documents are first retrieved from the document case base using the k-nearest neighbor technique, which is the main algorithm of case-based reasoning. Then, TFIDF method, which is the traditional vector model in information retrieval technique, is applied to the query document and the k retrieved documents to classify the query document. We call this procedure 'CB_TFIDF' method. The result of our research showed that the classification accuracy of CB_TFIDF was similar to that of traditional TFIDF method. However, the average time for classifying one document decreased remarkably.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.467-470
• /
• 2004

MANPro-based control is a novel control paradigm aimed at intelligent manufacturing systems on the basis of mobile agent-based negotiation process (MANPro). MANPro is a negotiation mechanism based on the agent-based control architecture and, especially, it adapts a mobile agent system called N-agent for the negotiation process. N-agent travels around the network of distributed manufacturing systems to acquire information, and it makes a decision for system control according to the obtained information. MANPro includes communication architecture and information architecture for intelligent shop floor control. MANPro also considers the following issues: (1) negotiation mechanism, (2) single-agent internal strategic policies, and (3) information model. Communication architecture concerns the first issue of the negotiation mechanism. It provides information exchanging mechanism with functional modules. In specific, N-agent is equipped with an intelligent reasoning engine with a built-in knowledge base. This reasoning engine is closely related to the single-agent internal strategic policies of the second issue. Finally, ontology-based information architecture addresses information models and provides a framework for information modeling on negotiation. In this paper, these three issues are addressed in detail and a framework of MANPro-based control is also proposed.

• Journal of The Korean Association For Science Education
• /
• v.17 no.3
• /
• pp.323-332
• /
• 1997

In order to use analog more systematically in science class, an instructional model was designed on the basis of analogical reasoning processes (encoding, inference, mapping, application, and response) in the Sternberg's component process theory. The model has five phases (introducing target context, cue retrieval of analog context, mapping similarity and drawing target concept, application, and elaboration), and the instructional effects of using the model upon students' comprehension of science concepts and motivation level of learning were investigated. The treatment and control groups (1 class each) were selected from 8th-grade classes and taught about chemical change and chemical reaction for the period of 10 class hours. The treatment group was taught with the materials based on the model, while the control group was taught in traditional instruction without using analog. Before the instructions, modified versions of the Patterns of Adaptive Learning Survey and the Group Assessment of Logical Thinking were administered, and their scores were used as covariates for students' conceptions and motivational level of learning, respectively. Analogical reasoning ability test was also administered, and its score was used as a blocking variable. After the instructions, students' conceptions were measured by a researcher-made science conception test, and their motivational level of learning was measured by a modified version of the Instructional Materials Motivation Scale. The results indicated that the adjusted mean score of the conception test for the treatment group was significantly higher than that of the control group at .01 level of significance. No significant interaction between the instruction and the analogical reasoning ability was found. Although the motivational level of learning for the treatment group was higher than that for the control group, the difference was found to be statistically insignificant. Educational implications are discussed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.1
• /
• pp.7-14
• /
• 2017

Theoretically, it is known that common beliefs and/or common knowledge cannot be attained in asynchronously distributed multiagent environments, however, it show that some propositions with deadlines can be attained as common beliefs among a set of fully trusted agents even when they communicate to each other asynchronously. Generally, in the multiagent environment, the attainment of common beliefs is approached as a problem of communication, and for the common beliefs paradox that the common beliefs is not attained on a system without communication time restriction is applied to loose coarser granularity and it prove that forming common beliefs is possible by relaxing necessary requirements through the KakaoTalk chatting model. I also experimented with the reasoning function that confirms the common beliefs by inquiring about the common belief generated by implementing the inference function in each agent of the KakaoTalk chatting model. Through utilizing metalogic programming, a formalization of the presentation and reasoning of common beliefs has been achieved, and the group chatting model of KakaoTalk was adopted in experiments to show that common beliefs can be formed among distributed agents using asynchronous communication.

• Journal of KIISE:Databases
• /
• v.27 no.2
• /
• pp.141-150
• /
• 2000

There are three significant drawbacks in extant fuzzy rule-based expert system languages. First, they lack the functionality of composite object inference. Second, they do not support fuzzy reasoning semantically easy to understand and conceptually simple to use. Third, knowledge representation and reasoning style of their model have a great semantic gap with those of current database models. Therefore, it is very difficult for the two models to be seamlessly integrated with each other. This paper provides the formal specification of a fuzzy object inference model to solve the three drawbacks. GIS(Geographic Information System) application domain is used to demonstrate that our model naturally models complex GIS information in terms of composite objects and successfully performs fuzzy inference between them.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2001.05a
• /
• pp.184-197
• /
• 2001

In this paper, we provide the formal specification of a fuzzy object inference language and propose ICOT(Integrated C-Object Tool) as its implementation for knowledge-based programming with the disjunctive fuzzy information. The novelty of our model is that it seamlessly combines object inference and fuzzy reasoning into a unified framework without compromising a compatibility with extant databases, especially object-relational ones. In this model most of the object-oriented paradigm is successfully expressed in terms of relational constructs, tailoring fuzzy reasoning style to be well suited to the framework of the databases. It turns out to be useful in preserving its conceptual simplicity as well, since simple-to-use is one of important criteria in designing the databases. Additionally this model considerably enhanced the semantic expressiveness of data allowing disjunctive fuzzy information.

• Journal of KIISE
• /
• v.41 no.10
• /
• pp.762-773
• /
• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• The Journal of Korean Association of Computer Education
• /
• v.9 no.3
• /
• pp.121-128
• /
• 2006

This study suggested the new e-learning systems based on agent to provide an adaptable learning. In Semantic Web environment, to develop an ontology and an intelligent agent is essential for an adaptable e-learning systems. Especially, to develop a reasoning engine using analysis of learning content and learners' information can offer an effective e-learning system. Therefore, we developed an applying model to an adaptable e-learning systems and the various ontologies for Semantic Web environment. Moreover, we analyzed and developed ontologies within the framework of learning domain, a learner and interface. Further, we implemented an intelligent e-learning for applying an agent's reasoning. Through this system proposed, we suggested the new e-learning systems model for Semantic Web environment.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.516-525
• /
• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. 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. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.2
• /
• pp.100-109
• /
• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model