• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.319-326
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• 2000

Expert systems using uncertain and ambiguous knowledge are not of the recent interests about uncertainty problem for performing inference similar to the decision making of a human expert. Human factors on rule-based systems often involve uncertain information. Expert systems had been used the methods of conflict resolution in a rule conflict situation, but this methods not properly solved the rule conflict. If a human expert appends a new rule to an original rule base, the rule base rightly causes a rule conflict. In this paper, the problem of rule conflict is regarded as one in which uncertainty of information is fundamentally involved. In the reduction of problem with uncertainty, we propose an enhanced rule ordering method, which improve the rule ordering method using Dempster-Shafer theory. We also propose a combinative index, which involve human factors of experts decision making.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.231-238
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• 1993

A rules of fuzzy control is to represent an expert‘s and engineer‘s ambiguous control knowledge of system with some lingustic rules. This rule is very difficult to represent perfectly because expert‘s knowledge is not precise and the rule is not perfect. We propose the fuzzy reasoning and learning to upgrade precision of imperfect rules successively after system running. In the proposed method, the precision of the backward part of a fuzzy rule is improved by back propagation learning method. Also, the method reasons the compatibility degree of the forward part of fuzzy rule by associative memory method. This method this is successfully applied to design auto-parking fuzzy controller in which expert‘s technology and knowledge are required in the limited area.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.783-790
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• 2002

This paper proposes a fuzzy Pr/T net representation of interval-valued fuzzy set reasoning, where fuzzy production rules are used for knowledge representation, and the belief of fuzzy production rules are represented by interval-valued fuzzy sets. The presented interval-valued fuzzy reasoning algorithm is much closer to human intuition and reasoning than other methods because this algorithm uses the proper belief evaluation functions according to fuzzy concepts in fuzzy production rules.

• Korean Journal of Child Studies
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• v.26 no.2
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• pp.1-14
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• 2005

This study investigated moral judgment and moral reasoning about aggressive behavior by intention, presentation of results of aggressive behavior, and age of child. Forty-four 3-year old and forty-six 5-year-old day-care children in Seoul and Kyonggi Province were interviewed individually with 20 pictorial tasks. Data analysis was by frequencies, percentiles, means, standard deviations, paired t-test, and ANOVA(repeated measures). Both age groups judged instrumental and resentment-based types of aggression to be worse than prosocial or rule observance-based aggression. Both age groups judged aggressive behavior to be worse when results of aggression were presented. Five-year-olds judged aggression to be worse on instrumental than on retributive types of intent. Level of reasoning on aggressive behavior was lowest in cases of satisfying resentment Level of reasoning about aggression increased with age.

• Spatial Information Research
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• v.9 no.2
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• pp.171-184
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• 2001

The nature of ill-defined planning problems makes expert systems difficult to acquire and represent knowledge for decision making in urban planning processes. In order to resolve these problems, a case-based reasoning method was applied to develop a spatial analysis system for urban planning. A case study was conducted in a residential land use planning process. The result of the study revealed the effectiveness of reasoning by the spatial analysis system and the possibility of its future application. More accumulation of information on other successful cases should be sought to yield better results

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.99-108
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• 2007

Context-Aware system using ontology is able to infer a context from help by reasoning engine. It can solve the ambiguity of intention reasoning of context-aware system as it is being made a reasoning rule followed reasoning grammar and being helped by reasoning engine, Also, it has a merit that is easy to apply to new environment by excluding reasoning algorithm from the program. In this paper, we are present context-aware system using ontology, We have tested and implemented it at home basis environment to verify of its effectiveness.

• Journal of KIISE
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• v.43 no.3
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• pp.370-379
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• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.227-230
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• 2003

We can not distinguish semantism of the feature of the current language “big”, “small”, “beautiful”. But we study artificial linguistic interface work and convert natural language to digital binary linguistic theory, we should define the basical conversion process. When we utilize the sum of product fuzzy theory and the visible numerical value, we can establish reasoning rule of input language. Fuzzy theory should be converted to general resulting rule.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is 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. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.423-432
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• 2011

In this paper, we introduce XOnto-Apriori algorithm which is an extension of the Onto-Apriori algorithm. The extended algorithm is designed to improve the conventional algorithm's problem of comparing only identifiers of transaction items by reasoning transaction properties of the items which belong in the same category. We show how the mining algorithm works with a smartphone application recommender system based on our extended algorithm to clearly describe the procedures providing personalized recommendations. Further, our simulation results validate our analysis on the algorithm overhead, precision, and recall.