• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.49-53
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• 2010

e-learning and m-learning have some problems that data transmission frequently discontinuously, communication cost increases, the computation speed of mass data drops, battery limitation in the mobile learning environments. In this paper, we propose the PULIMS for u-learning systems. The proposed system intellectualize the education environment using intelligent mobile agent, supports the customized education service, and helps that learners feasible access to the education information through mobile phone. We can see the fact that the efficience of proposed method is outperformed that of the conventional methods. The PULIMS is new technology that can be used to learn whenever and wherever learners want in Ubiquitous education environment.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.595-600
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• 2001

Recently, WBI model which is based on web has been proposed in the part of the new activity model of teaching-learning. The demand for the customized coursewares which is required from the learners is increased, the needs of the efficient and automated education agents in the web-based instruction are recognized. But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the learning weakness which is observed in the continuous feedback of the course. In this paper we propose design of multi-agent system for course scheduling of learner-oriented using weakness analysis algorithm. First, proposed system monitors learner's behaviors constantly, evaluates them, and calculates his accomplishment. From this accomplishment, the multi-agent schedules the suitable course for the learner. The learner achieves an active and complete learning from the repeated and suitable course.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.263-270
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• 2016

In this study, it was attempted to develop a grade-customized statistical analysis system that can be operated by a teacher without professional knowledge of statistics by utilizing profiling in the education sector. For this, with the convergence of techniques of profiling into the education sector, it examined the elements necessary for building a customized student multidimensional analysis system. Referring to the overall configuration and the current state to build multidimensional analysis system utilizing practical profiling, it showed the implementation result of the algorithm applied to each statistical method, and presented the differences and superiority to existing systems. Once the system based on the proposed techniques is built, considering differences of students' needs and abilities and clarifying precise objectives and standards, with the improvement of satisfaction in public education, it is possible not only to reduce expense of prior and private learning but also realize self-directed learning suitable to one's learning ability and aptitude.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1046-1053
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• 2003

Recently various new model of teaching-learning as web based education system has been proposed. The demand for the customized courseware which is required from the learners is increased, the needs of the efficient and automated education agents in the web-based instruction are recognized. But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the teaming weakness which is observed in the continuous feedback of the course. In this paper we propose a multi-agent system for course scheduling of learner-oriented using weakness analysis algorithm. First proposed system analyze learner's result of evaluation and calculates teaming accomplishment. From this accomplishment the multi-agent schedules the suitable course for the learner The learner achieves an active and complete learning from the repeated and suitable course.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.117-128
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• 2004

Compared with traditional face-to-face instruction, online learning causes learners to experience more severe feeling of isolation and results in higher dropout rate. This is due to the lack of interaction, sense of belonging, membership, interdependency, cooperation among members and social environment that enables persistence in online learning. Therefore, it is very important for grouping e-learning community to lower the dropout rate and eliminate feeling of isolation. In this paper, the research has been done on the inclination test list to be applied for grouping the desirable learning community. And on the basis of this research, the grouping system for e-learning community(GSE) based on intelligent multi agents for an inclination test using homogeneous and heterogeneous items has been developed. GSE system has such properties that construct a personalized user profile by an agent, and then make groupings according to users' inclination. When this system was evaluated, about 88% of learners were satisfied, and they wanted the group not to be disorganized but to be maintained.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.707-720
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• 2021

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.407-412
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• 2004

The e-learning system uses multimedia content for efficiency of a education and learner's convenience. With the web based e-learning, various multimedia services are difficult on account of physical factors such as network state and system performance. Also on the off line based e-learning, we have defects an insufficiency of interactions among learners and instructors, and an insufficiency of an adaptation about the latest information. In this study, we designed a multi-tier e-learning system using hybrid multimedia method, The proposed system can provide interaction and adaptability such as web based systems, and it can provide various multimedia content such as off line based systems. In experiments results, we know the proposed system improved users' convenience and an adaptability about the latest information, and interactions among learners and instructors when compare with existing e-learning system.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.115-120
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• 2022

The main purpose of the study is to identify the key aspects of the use of innovative distance learning technologies in the training of biology students. Currently, there is a modernization, the evolution of the education system from a classical university to a virtual one, from lecture material teaching to computer educational programs, from a book library to a computer one, from multi-volume paper encyclopedias to modern search databases. During studies in higher education, distance learning ensures the delivery of information in an interactive mode through the use of information and communication technologies. The main disadvantage of distance learning is the emotional interaction of the teacher with students. It is necessary to increase the level of methodological developments for independent studies of students. The methodology includes a number of theoretical methods. Based on the results of the study, the main elements of the use of innovative distance learning technologies in the training of biology students were identified.