• 한국콘텐츠학회논문지
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• 제9권10호
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• pp.51-58
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• 2009

강화학습은 최적의 행동정책을 구하는 최적화 문제로 주어진 환경과의 상호작용을 통해 받는 보상 값을 최대화하는 것이 목표이다. 특히 단일 에이전트에 비해 상태공간과 행동공간이 매우 커지는 다중 에이전트 시스템인 경우 효과적인 강화학습을 위해서는 적절한 행동 선택 전략이 마련되어야 한다. 본 논문에서는 멀티에이전트의 효과적인 행동 선택과 학습의 수렴속도를 개선하기 위하여 퍼지 추론 기반의 멀티에이전트 강화학습 모델을 제안하였다. 멀티 에이전트 강화학습의 대표적인 환경인 로보컵 Keepaway를 테스트 베드로 삼아 다양한 비교 실험을 전개하여 에이전트의 효율적인 행동 선택 전략을 확인하였다. 제안된 퍼지 추론 기반의 멀티에이전트 강화학습모델은 다양한 지능형 멀티 에이전트의 학습에서 행동 선택의 효율성 평가와 로봇축구 시스템의 전략 및 전술에 적용이 가능하다.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.365-373
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• 2005

유비쿼터스 환경을 위한 웹 기반 교육 시스템으로서 다양한 온라인 학습에 대한 새로운 교수 모형이 요구되고 있다. 또한, 학습자의 요구에 맞는 코스웨어의 주문이 증가되고 있는 추세이며 그에 따라 웹 기반 교육시스템에 효율적이고 자동화된 교육 에이전트의 필요성이 인식되고 있다. 그러나 현재 연구되고 있는 많은 교육 시스템들은 학습자 성향에 맞는 코스를 적절히 서비스해 주지 못할 뿐 아니라 지속적인 피드백과 학습자가 코스를 학습함에 있어서 취약한 부분을 재학습 할 수 있도록 도와주는 서비스를 원활히 제공하지 못하고 있다. 본 논문에서는 취약성 분석 알고리즘을 이용한 학습자 중심의 유비쿼터스 환경팩터를 통한 코스 스케줄링 멀티 에이전트 시스템을 제안한다. 제안한 시스템은 먼저 학습자의 학습 평가 결과를 분석하고 학습자의 학습 성취도를 계산하며, 이 성취도를 에이전트의 스케줄에 적용하여 학습자에게 적합한 코스를 제공하고, 학습자는 이러한 코스에 따라 능력에 맞는 반복된 학습을 통하여 적극적인 완전학습을 수행하게 된다.

• 산업경영시스템학회지
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• 제47권2호
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• pp.74-83
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• 2024

The threat of North Korea's long-range firepower is recognized as a typical asymmetric threat, and South Korea is prioritizing the development of a Korean-style missile defense system to defend against it. To address this, previous research modeled North Korean long-range artillery attacks as a Markov Decision Process (MDP) and used Approximate Dynamic Programming as an algorithm for missile defense, but due to its limitations, there is an intention to apply deep reinforcement learning techniques that incorporate deep learning. In this paper, we aim to develop a missile defense system algorithm by applying a modified DQN with multi-agent-based deep reinforcement learning techniques. Through this, we have researched to ensure an efficient missile defense system can be implemented considering the style of attacks in recent wars, such as how effectively it can respond to enemy missile attacks, and have proven that the results learned through deep reinforcement learning show superior outcomes.

• IEIE Transactions on Smart Processing and Computing
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• 제5권6호
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.164.1-164
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• 2001

Q-learning is a recent reinforcement learning algorithm that does not need a modeling of environment and it is a suitable approach to learn behaviors for autonomous agents. But when it is applied to multi-agent learning with many I/O states, it is usually too complex and slow. To overcome this problem in the multi-agent learning system, we propose the successive Q-learning algorithm. Successive Q-learning algorithm divides state-action pairs, which agents can have, into several Q-functions, so it can reduce complexity and calculation amounts. This algorithm is suitable for multi-agent learning in a dynamically changing environment. The proposed successive Q-learning algorithm is applied to the prey-predator problem with the one-prey and two-predators, and its effectiveness is verified from the efficient avoidance ability of the prey agent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• 한국지구과학회지
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• 제41권3호
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• pp.291-306
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• 2020

이 연구에서는 다층 서답형 문항을 이용하여 태양계 구조에 대한 학습 발달과정을 개발하고 그 타당성을 검증하고자 하였다. 이를 위해 Wilson(2005)이 제안한 구인 모델링 방식을 적용하여 '태양계 구성원', '태양계 행성의 크기와 거리의 경향성', '태양계 모델링'을 발달 변인(progress variables)으로 설정하고 각각에 대한 다층 서답형 문항을 개발하여 검사지로 구성하였다. 개발된 문항을 초등학교 5학년 150명을 대상으로 '태양계와 별' 단원 수업의 사전 및 사후에 적용하였다. 평가 결과를 기술하기 위해 각각의 평가 문항에 대한 학생 응답을 범주화 하는 과정을 거쳤으며, 이범주들을 구인별로 5개 수준으로 분류하였다. Rasch 모델의 부분점수 모형을 적용하여 작성된 Wright map을 분석함으로써 학생들의 응답 결과를 기반으로 작성된 학습 발달과정의 수준이 적절한지 검토하였다. 또한, 수업 전후 학생들의 수준 변화를 추적함으로써 학습 발달과정에서 설정한 가설적인 경로의 타당성을 검증하였다. 연구 결과는 다음과 같다: 다층 서답형 문항을 이용한 상향식 연구방법으로 초등학교에 적용할 수 있는 태양계 구조에 대한 경험적 학습 발달과정을 정교하게 설정할 수 있었다. 그리고 학습 발달과정의 구인 타당도가 높게 나타나며 학생들의 발달이 학습 발달과정을 따라 변화하는 것으로 나타났다.

• 컴퓨터교육학회논문지
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• 제11권6호
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• pp.95-102
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• 2008

본 연구에서 SDG를 사용한 교육 퍼즐구현을 통하여 협동학습을 컴퓨터교육에 적용하였다. SDG란 하나의 컴퓨터 디스플레이에 다중 입력장치로 협동적인 작업을 할 수 있는 시스템을 말한다. SDG 기반의 협동학습을 통해 학습자들은 협동 학습을 수행하게 된다. SDG를 이용한 협동학습이 단일 디바이스를 이용한 개별 학습보다 우월하다는 기존의 연구가 있다. 이를 바탕으로 협동학습을 이용한 퍼즐게임을 구현하였다.

• 한국시뮬레이션학회논문지
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• 제3권2호
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• pp.69-76
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• 1994

Traditionally, simulation-based learning games which are known as flight-simulators have been constructed as a black-box game. Within a black-box game, game-players can view and modify only a part of model parameters. Game-players cannot change the structure of a simulation model. In a black-box game, game-players cannot understand and learn the system structure which is responsible for the system behavior. In this paper, the multi-tasking at the level of operating systems is exploited to enhance the transparency of simulation-based learning game. The white-box game or transparent-box game allows game-players ot view and modify the model structure. The multi-tasking solution for white-box learning game is implemented with Smalltalk language on MS-/windows operating system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권2호
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.