• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.39-45
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• 2007

In this paper, we developed an intelligent web based e-learning system based on multi agents. To do development of the system, we applied an inclination test that is based on the education theory to do grouping the desirable e-learning community. The proposed system, Intelligent Web based e-learning Multi Agent System (IMAS), is used the multi agents paradigm including learning manner by neural network for grouping of e-learning community and a new distributed multi agent framework proposed here.

• Journal of The Korean Association of Information Education
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• v.5 no.3
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• pp.351-363
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• 2001

The established Web Based Learning System had not encouraged Multi-Learner Interaction. The System for Multi-Learner Interaction proposed an alternative measure. By now, this System for Multi-Learner Interaction have applied the sphere of Web Based Learning. But, Actually, the Multi-Learner's Interaction and Feedback of this System has a few effective and confidence, because this System has much time gap between Interaction and Feedback of Multi-Learner primarily. This Research propose an answer of this Problem, Web Based Real Time Quiz-game Type Learning System for Multi-Learner Interaction. Moreover, this System expect increased concentration ability for Learning and strong motivation of Learning for the greatest Learning effects in a Subject Solution course of Learners with founding Web Based Quiz-game Type Learning. So this Research will design and implement the Web Based Real Time Quiz-game Type Learning System for Multi-Learner Interaction.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.41-53
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• 2000

The purpose of the paper is communication between multi-agents for student's learning at web based collaborative learning. First, this study investigated the whole contents and characteristics of an agent system and discussed KQML, communication language between multi-agents. Also, we suggested architecture of an agent based system for collaborative learning and interaction method between agents using KQML. We design어 and implemented collaborative learning system using Java programming language, and we also demonstrate the efficiency of collaborative learning system by communication between multi-agents through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.619-624
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• 2014

This paper introduces a robot-based learning system which is designed to teach multiplication to children. In addition to a small humanoid and a smart device delivering educational content, we employ a type of mixed-initiative operation which provides enhanced multi-modal cognition to the r-learning system through human intervention. To investigate major factors that influence people's intention to use the r-learning system and to see how the multi-modality affects the connections, we performed a user study based on TAM (Technology Acceptance Model). The results support the fact that the quality of the system and the natural interaction are key factors for the r-learning system to be used, and they also reveal very interesting implications related to the human behaviors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.50-55
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• 2021

Multi-path system is a system in which utilizes various networks simultaneously. It is expected that multi-path system can enhance communication speed, reliability, security of network. In this paper, we focus on path selection in multi-path system. To select optimal path, we propose deep reinforcement learning algorithm which is rewarded by the round-trip-time (RTT) of each networks. Unlike multi-armed bandit model, deep Q learning is applied to consider rapidly changing situations. Due to the delay of RTT data, we also suggest compensation algorithm of the delayed reward. Moreover, we implement testbed learning server to evaluate the performance of proposed algorithm. The learning server contains distributed database and tensorflow module to efficiently operate deep learning algorithm. By means of simulation, we showed that the proposed algorithm has better performance than lowest RTT about 20%.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.584-587
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• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.91-99
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• 2013

In these days, the smart devices are being developed and used repeatedly. But almost E-learning System did not provide these smart devices environment and user's E-learning space is changing from Single-screen environment to multi-Screen environment. Accordingly we configure smart E-learning space based on multi-screen and integrate the multi-media contents for the Smart E-learning service. We present the synchronization techniques to make the multi-screen environment for smart space with Timed Link and design the Timed Button to relate each Screen which is using multi-media e-learning contents.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.751-756
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• 2013

This paper describes the design and implementation of the multi-function learning community system. Recent trends and related researches regarding the learning community system are surveyed and analyzed. The function sections, data flows, database tables and system interface are designed. Manager and user's modes are implemented and we compare the system functions with other learning communities.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.49-57
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• 2008

In this paper, we suggested a methodology that can be used by an agent to learn models of other agents in a multi-agent system. To construct these model, we used influence diagram as a modeling tool. We present a method for learning models of the other agents at the decision nodes, value nodes, and chance nodes in influence diagram. We concentrated on learning of the other agents at the value node by using neural network learning technique. Furthermore, we treated anti-air units in anti-air defense domain as agents in multi. agent system.