• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.64-72
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• 1997

A strategy is proposed for the systematic synthesis of goal-tree to support the operation of abnormal situation in chemical plant. A knowledge base using the heuristics of operators is organized for synthesizing goal tree to take appropriate safety precautions with properties of accident. A computer-based system which utilizes artificial intelligence technique is developed to evaluate the effectiveness of the methodology and applied to the model plant.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.257-271
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• 2006

Recently, flash memories are one of best media to support portable computer's storages in mobile computing environment. The features of non-volatility, low power consumption, and fast access time for read operations are sufficient grounds to support flash memory as major database storage components of portable computers. However, we need to improve traditional Indexing scheme such as B-Tree due to the relatively slow characteristics of flash operation as compared to RAM memory. In order to achieve this goal, we devise a new indexing scheme called F-Tree. F-Tree improves tree operation performance by compressing pointers and keys in tree nodes and rewriting the nodes without a slow erase operation in node insert/delete processes. Based on the results of the performance evaluation, we conclude that F-Tree indexing scheme outperforms the traditional indexing scheme.

• IE interfaces
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• v.15 no.4
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• pp.460-473
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• 2002

The objective of this paper is to design a Goal Alternatives Disposition System having three main subsystems for setting, evaluating and selecting goal alternatives. For setting of goal alternatives, System Alternatives Tree(SAT) structure will be developed, which has a computation algorithm for setting decision alternatives by the concept of System Priority Number(SPN). For evaluating and selecting of goal alternatives; First, Normative and Exploratory Priority Indices which consider technical performance to the goal, cost and feasibility are developed respectively. Second, Integrated Priority Index is built up to determine the total priority of the Goal Alternatives Disposition(GAD) system. For the design and verification of the GAD system, technological forecasting structure theory, systems engineering methodology will be used.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.59-68
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• 1998

The goal of data mining is to develop the automatic and intelligent tools and technologies that can find useful knowledge from databases. To meet this goal, we propose an efficient data mining algorithm based on the fuzzy decision tree. The proposed method combines comprehensibility of decision tree such as ID3 and C4.5 and representation power of fuzzy set theory. So, it can generate simple and comprehensive rules describing data. The proposed algorithm consists of two stages: the first stage generates the fuzzy membership functions using histogram analysis, and the second stage constructs a fuzzy decision tree using the fuzzy membership functions. From the testing of the proposed algorithm on the IRIS data and the Wisconsin Breast Cancer data, we found that the proposed method can generate a set of fuzzy rules from data efficiently.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.134-144
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• 1996

In this paper, we propose a new access method, called the HG-tree, to support indexing and retrieval by image content in large image databases. Image content is represented by a point in a multidimensional feature space. The types of queries considered are the range query and the nearest-neighbor query, both in a multidimensional space. Our goals are twofold: increasing the storage utilization and decreasing the area covered by the directory regions of the index tree. The high storage utilization and the small directory area reduce the number of nodes that have to be touched during the query processing. The first goal is achieved by absorbing splitting if possible, and when splitting is necessary, converting two nodes to three. The second goal is achieved by maintaining the area occupied by the directory region minimally on the directory nodes. We note that there is a trade-off between the two design goals, but the HG-tree is so flexible that it can control the trade-off. We present the design of our access method and associated algorithms. In addition, we report the results of a series of tests, comparing the proposed access method with the buddy-tree, which is one of the most successful point access methods for a multidimensional space. The results show the superiority of our method.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.267-269
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• 2006

Recently, flash memories are one of best media to support portable computer's storages in mobile computing environment. The features of non-volatility, low power consumption, and fast access time for read operations are sufficient grounds to support flash memory as major database storage components of portable computers. However, we need to improve traditional Indexing scheme such as B-Tree due to the relatively slow characteristics of flash operation as compared to RAM memory. In order to achieve this goal, we devise a new indexing scheme called F-Tree. F-Tree improves tree operation performance by compressing pointers and keys in tree nodes and rewriting the nodes without a slow erase operation in node insert/delete processes.

• International Journal of Safety
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• v.7 no.1
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• pp.15-20
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• 2008

The primary goal of this study is to analyze fire hazards of electric home appliances such as electric iron and electric heater using fault tree analysis(FTA). A fault tree(FT) is constructed and used to analyze fire hazards in electric home appliances. The fault tree is built from events that may occur in electric home appliances. The failure rate of basic events are derived from the value of experimental results and reference. And an algorithm analyzing fire in electric home appliances is suggested. We show how fault tree analysis, carried out by means of failure rate, is able to diagnose fire hazards of electric home appliances in a precise manner. We present numerical results such as fire probability of electric home appliances, importance measure, fire cause, etc. It can be helpful in preventing the fire hazards in electric home appliances.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.432-439
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• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.543-559
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• 2017

Tree-based regression and classification ensembles form a standard part of the data-science toolkit. Many commonly used methods take an algorithmic view, proposing greedy methods for constructing decision trees; examples include the classification and regression trees algorithm, boosted decision trees, and random forests. Recent history has seen a surge of interest in Bayesian techniques for constructing decision tree ensembles, with these methods frequently outperforming their algorithmic counterparts. The goal of this article is to survey the landscape surrounding Bayesian decision tree methods, and to discuss recent modeling and computational developments. We provide connections between Bayesian tree-based methods and existing machine learning techniques, and outline several recent theoretical developments establishing frequentist consistency and rates of convergence for the posterior distribution. The methodology we present is applicable for a wide variety of statistical tasks including regression, classification, modeling of count data, and many others. We illustrate the methodology on both simulated and real datasets.