• Asia pacific journal of information systems
• /
• v.17 no.1
• /
• pp.33-58
• /
• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.166-169
• /
• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• International Journal of Advanced Culture Technology
• /
• v.11 no.4
• /
• pp.286-294
• /
• 2023

We studied learners' perceptions and learning styles of project research activities in the chemical field conducted by 54 science high school students. In a survey of students' perceptions of task research, positive responses were found in "internal motivation," "cooperation," "task solving," and "tenacity and immersion," and statistically significant differences were found in "self-directedness," "cooperation," and "tenacity and immersion" by year. The 'lower' group responded most positively in the 'cooperation' category, and the 'higher' group responded most positively in the 'task solving' category. As a result of investigating the learning styles of the students who conducted the task research, it was found in the order of assimilator, converger, accommodator, and diverger. The assimilators showed the characteristic of systematically and scientifically approaching the problem. Convergers were found to have excellent problem-solving and decision-making ability, are practical, and have experimental-based thinking characteristics. In this study, the characteristics of science high school students showed well in the results of the learning style performed.

• The KIPS Transactions:PartB
• /
• v.9B no.2
• /
• pp.155-162
• /
• 2002

Reinforcement learning is a learning method that uses trial-and-error to perform Learning by interacting with dynamic environments. It is classified into online reinforcement learning and delayed reinforcement learning. In this paper, we propose an online reinforcement learning system (ONRELS : Outline REinforcement Learning System). ONRELS updates the estimate-value about all the selectable (state, action) pairs before making state-transition at the current state. The ONRELS learns by interacting with the compressed environments through trial-and-error after it compresses the state space of the mage environments. Through experiments, we can see that ONRELS can search the shortest path faster than Q-learning using TD-ewor and $Q(\lambda{)}$-learning using $TD(\lambda{)}$ in the maze environments.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.991-997
• /
• 2020

Reinforcement learning has been applied mainly in sequential decision-making problems. Especially in recent years, reinforcement learning combined with neural networks has brought successful results in previously unsolved fields. However, reinforcement learning using deep neural networks has the disadvantage that it is too complex for immediate use in the field. In this paper, we implemented path planning algorithm for mobile robots using Q-learning, one of the easy-to-learn reinforcement learning algorithms. We used real-time Q-learning to update the Q-table in real-time since the Q-learning method of generating Q-tables in advance has obvious limitations. By adjusting the exploration strategy, we were able to obtain the learning speed required for real-time Q-learning. Finally, we compared the performance of real-time Q-learning and DQN.

• Korean Journal of Childcare and Education
• /
• v.16 no.5
• /
• pp.27-45
• /
• 2020

Objective: The purpose of this study is to develop a music making program that utilizes the characteristics of the picture books medium through the ADDIE model so that teachers can easily apply it to children, and to determine whether it affects children's musical aptitude and music creativity. Methods: The Participants of the study were 42 five-year-old children attending a daycare in Seoul, of which 20 were in the experimental group and 22 in the comparative group. The experimental group participated in a music production program using picture books, and during the same period, the comparison group participated in music activities suggested by the Nuri Course. Using the SPSS 22.0 program, average, standard deviation, independent sampling t-test, and ANCOVA(Analysis of Covariance) were calculated. Results: The music making program using picture books improved children's rhythm and tone which are the sub-items of music aptitude. The music making program enhanced children's music flexibility, music creativity, music logic which are the sub-items of music creativity. Conclusion/Implications: The music making program presented systemic teaching-learning method with which teachers explained the modeling and practiced from simple activities to various activities repeatedly in order to make teachers approach music making more easily. It is recommendable to make the music making program by using I-pad and computers.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.45 no.2
• /
• pp.271-284
• /
• 2021

This study presents the educational utility of blended learning by analyzing the effectiveness of learning after class by blending team project learning and smart device app-based learning methods. Qualitative analysis and survey analysis were conducted and the results were as follows. First, team project activities based on task resolution were conducted freely through detailed activities such as sharing roles, planning meetings, and coordinating opinions. Team activities were carried out with respect and consideration, team member bonding, and a sense of responsibility. Second, the smart device app is recognized as a medium for work and communication, and fast feedback has been made, making it highly impactful on classroom activities. Third, in terms of learning satisfaction, most learners showed an interest in the course and were satisfied with the project results. The smart device app was used as a learning and communication medium for personal and team activities and was analyzed as a blended method applicable to classes that conduct practical activities as an efficient tool to further activate project activities.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.1
• /
• pp.65-72
• /
• 2007

This paper aims to provide information to generate a statistical load model of an educational server by analyzing workload of an e-Learning sewer at Dankook University. The result of the analysis shows file size distribution, access frequency and transmission volume for each file type, access interval, changes in preference and clients access rate by networks. In particular, it had different results from previous studies about video file's size distribution and file distribution based on access frequency. This is because the characteristics of e-learning are influenced by using authoring tools for making into video file and by freeing the number of students who register for a course. The result in this paper can be used as a basic data for studies designed to improve e-learning system architecture and server performance.

• Language and Information
• /
• v.7 no.1
• /
• pp.87-99
• /
• 2003

This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach.

• Journal of the Korean Regional Science Association
• /
• v.15 no.1
• /
• pp.93-116
• /
• 1999

Recently the concept of learning has become very fashionable among academics from different economic disciplines. Economic geographers and spatial planners joined this fashion by increasingly speaking about the 'learning region'. This paper makes clear that this learning region'. This paper makes clear that this learning region concept has been launched from three angles; as spatial outcome of grand societal changes, as spatial concentration of entrepreneurial learning for innovation and as regional development concept. Despite the deficits and flaws such a young concept is faced with, such as vague definitions, the lack of empirical research and an insufficiently clear separation from existing concepts, the learning region concept might provide economic geography with more insight in agglomeration effects, stronger links with policy-making and more knowledge on path dependency and thus on unravelling the distinction between 'good' and 'bad' industrial agglomerations.