• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.170-175
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• 1998

Transporting a large table using multiple robotic agents requires at least two group behaviors of homing and herding which are to bo coordinated in a proper sequence. Existing GP methods for multi-agent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we use a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to co-evolve more complex behavior. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behavior which may not be solved by simple genetic programming.

• Annual Conference of KIPS
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• 2000.10a
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• pp.285-288
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• 2000

본 논문에서는 다중 에이전트(multi-agent) 환경에서 에이전트들의 행동을 효율적으로 조정 (coordination)하기 위해 강화 학습(reinforcement learning)을 이용하였다. 제안된 방법은 각 에이전트가 목표(goal)와의 거리 관계(distance relationship)와 인접 에이전트들과의 공간 관계(spatial relationship)를 이용하였다. 그러므로 각 에이전트는 다른 에이전트와 충돌(collision) 현상이 발생하지 않으면서, 최적의 다음 상태를 선택할 수 있다. 또한, 상태 공간으로부터 입력되는 강화 값이 0과 1 사이의 값을 갖기 때문에 각 에이전트가 선택한 (상태, 행동) 쌍이 얼마나 좋은가를 나타낼 수 있다. 제안된 방법을 먹이 포획 문제(prey pursuit problem)에 적용한 결과 지역 제어(local control)나. 분산 제어(distributed control) 전략을 이용한 방법보다 여러 에이전트들의 행동을 효율적으로 조정할 수 있었으며, 매우 빠르게 먹이를 포획할 수 있음을 알 수 있었다.

• Electronics and Telecommunications Trends
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• v.37 no.6
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• pp.32-42
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• 2022

Recently, decentralized approaches to artificial intelligence (AI) development, such as federated learning are drawing attention as AI development's cost and time inefficiency increase due to explosive data growth and rapid environmental changes. Collaborative AI technology that dynamically organizes collaborative groups between different agents to share data, knowledge, and experience and uses distributed resources to derive enhanced knowledge and analysis models through collaborative learning to solve given problems is an alternative to centralized AI. This article investigates and analyzes recent technologies and applications applicable to the research of multi-agent collaboration of AI bots, which can provide collaborative AI functionality autonomously.

• 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.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.137-149
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• 2020

Several multi-agent reinforcement learning (MARL) algorithms have achieved overwhelming results in recent years. They have demonstrated their potential in solving complex problems in the field of real-time strategy online games, robotics, and autonomous vehicles. However these algorithms face many challenges when dealing with massive problem spaces in sparse reward environments. Based on the centralized training and decentralized execution (CTDE) architecture, the MARL algorithms discussed in the literature aim to solve the current challenges by formulating novel concepts of inter-agent modeling, credit assignment, multiagent communication, and the exploration-exploitation dilemma. The fundamental objective of this paper is to deliver a comprehensive survey of existing MARL algorithms based on the problem statements rather than on the technologies. We also discuss several experimental frameworks to provide insight into the use of these algorithms and to motivate some promising directions for future research.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.1-14
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• 2019

Recent trends in deep reinforcement learning (DRL) have revealed the considerable improvements to DRL algorithms in terms of performance, learning stability, and computational efficiency. DRL also enables the scenarios that it covers (e.g., partial observability; cooperation, competition, coexistence, and communications among multiple agents; multi-task; decentralized intelligence) to be vastly expanded. These features have cultivated multi-agent reinforcement learning research. DRL is also expanding its applications from robotics to natural language processing and computer vision into a wide array of fields such as finance, healthcare, chemistry, and even art. In this report, we briefly summarize various DRL techniques and research directions.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• 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.