• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.731-740
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• 2004

In this paper we propose a fast negotiation agent system that guarantees the reciprocity of the attendants in a bilateral negotiation on the e-commerce. The proposednegotiation agent system exploits the incremental learning method based on an artificial neural network in generating a counter-offer and is trained by the previous offer that has been rejected by the other party. During a negotiation, the software agents on behalf of a buyer and a seller negotiate each other by considering the multi-attributes of a product. The experimental results show that the proposed negotiation system achieves better agreements than other negotiation agent systems that are operated under the realistic and practical environment. Furthermore, the proposed system carries out negotiations about twenty times faster than the previous negotiation systems on the average.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.226-234
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• 2022

With advances in autonomous vehicles and networked control, there is a growing interest in the consensus control of a multi-agents system to control multi-agents with distributed control beyond the control of a single agent. Since consensus control is a distributed control, it is bound to have delay in a practical system. In addition, it is often difficult to have a very accurate mathematical model for a system. Even though a reinforcement learning (RL) method was developed to deal with these issues, it often experiences slow convergence in the presence of large uncertainties. Thus, we propose a slide RL which combines the sliding mode control with RL to be robust to the uncertainties. The structure of a sliding mode control is introduced to the action in RL while an auxiliary sliding variable is included in the state information. Numerical simulation results show that the slide RL provides comparable performance to the model-based consensus control in the presence of unknown time-varying delay and disturbance while outperforming existing state-of-the-art RL-based consensus algorithms.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.249-256
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• 2003

The most important problems in the multi-agent system are to accomplish a goal through the efficient coordination of several agents and to prevent collision with other agents. In this paper, we propose a new control strategy for succeeding the goal of the prey pursuit problem efficiently. Our control method uses reinforcement learning to control the multi-agent system and consider the distance as well as the space relationship between the agents in the state space of the prey pursuit problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2451-2460
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• 2000

In order to minimize straightness error of deflected shaft, a geometric adaptive straightness controller system is studied. A multi-step straightening and a three-point bending process have been developed for the geometric adaptive straightness controller. Load-deflection relationship, on-line identification of variations of material properties, on-line springback prediction, and real-time hydraulic control methodology are studied for the three-point bending process. By deflection pattern analysis and fuzzy self-learning method in the multi-step straightening process, a straightening point and direction, desired permanent deflection and supporting condition are determined. An automatic straightening machine has been fabricated for rack bars by using the developed ideas. Validity of the proposed system is verified through experiments.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.112-119
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• 2003

Generally, PID controller is not suitable to control multi variable system because it is very difficult to tune the PID gains. However, this paper shows that it is not hard to tune the PID gains if we can find a Jacobian matrix of the system. The Jacobian matrix expresses the ratio of output variations according to input variations. It is possible to adjust the input values in order to reduce the output error using the Jacobian. When the colt function is composed of error related terms, the gradient approach can tune the PID gains to minimize the function. In simulation, a hydrofoil catamaran with two inputs and two outputs is applied as a multi variable system. We can easily get the multi variable PID controller by the proposed teaming method. When the controller is compared with LQR controller, the performance is as good as that of LQR controller with a modeling equation.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.430-438
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• 2001

In this paper, we present the design and implementation of a web-based distance learning system supporting the collaborative browsing. Our system consists of an education affair management system, a video conferencing server/client, a white-board server/client, a session manager and a web browser sharing system. Among other things, our collaborative web browser is unique and not found in any other system. The web browser shows synchronously the same web pages as the lecturer moves through them. Therefore, it allows the student to feel real-time surfing gust as the lecturer would. The session manager supports multi-user and multi-group, and integrates various synchronous collaborative component into one distance learning system by providing the same session data and information of users in a session group. Our collaborative browsing system can increase the efficiency of distance learning and provides the effect of learning in the same classroom by supporting various synchronous functionalities, such as collaborative browsing.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.56-64
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• 1998

Multi-layered perceptrons that are a nonlinear neural network model, have been widely used for various applications mainly thanks to good function approximation capability for nonlinear fuctions. However, for digital hardware implementation of the multi-layere perceptrons, the quantization scheme using "look-up tables (LUTs)" is commonly employed to handle nonlinear signmoid activation functions in the neworks, and thus requires large amount of storage to prevent unacceptable quantization errors. This paper is concerned with a new effective methodology for digital hardware implementation of multi-layered perceptrons, and proposes a "piecewise affine approximation" method in which input domain is divided into (small number of) sub-intervals and nonlinear sigmoid function is linearly approximated within each sub-interval. Using the proposed method, we develop an expression and an error backpropagation type learning algorithm for a multi-layered perceptron, and compare the performance with the quantization method through Monte Carlo simulations on XOR problems. Simulation results show that, in terms of learning convergece, the proposed method with a small number of sub-intervals significantly outperforms the quantization method with a very large storage requirement. We expect from these results that the proposed method can be utilized in digital system implementation to significantly reduce the storage requirement, quantization error, and learning time of the quantization method.quantization method.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.145-150
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• 2001

A Controlled Ecological Life Support System(CELSS) is essential for man to live a long time in a closed space such as a lunar base or a mars base. Such a system may be an extremely complex system that has a lot of facilities and circulates multiple substances,. Therefore, it is very difficult task to control the whole CELSS. Thus by regarding facilities constituting the CELSS as agents and regarding the status and action as information, the whole CELSS can be treated as multi-agent system(MAS). If a CELSS can be regarded as MAS the CELSS can have three advantages with the MAS. First the MAS need not have a central computer. Second the expendability of the CELSS increases. Third, its fault tolerance rises. However it is difficult to describe the cooperation protocol among agents for MAS. Therefore in this study we propose to apply reinforcement learning (RL), because RL enables and agent to acquire a control rule automatically. To prove that MAS and RL are effective methods. we have created the system in Java, which easily gives a distributed environment that is the characteristics feature of an agent. In this paper, we report the simulation results for material circulation control of the CELSS by the MAS and RL.