• 항공우주의학회지
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• 제31권2호
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• pp.33-37
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• 2021

In the era of the 4th industrial revolution, the aviation industry is also growing remarkably with the development of artificial intelligence and networks, so it is necessary to study a new concept of crew resource management (CRM), which is required in the process of operating state-of-the-art equipment. The automation system, which has been treated only as a tool, is changing its role as a decision-making agent with the development of artificial intelligence, and it is necessary to set clear standards for the role and responsibility in the safety-critical field. We present a new perspective on the automation system in the CRM program through the understanding of the autonomous system. In the future, autonomous system will develop as an agent for human pilots to cooperate, and accordingly, changes in role division and reorganization of regulations are required.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1050-1053
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• 2005

There is no agreed definition of intelligence. The ability to adapt to the environments is a kind of intelligence. Expert functionally recognize environment using their five senses, and acquire and memorize knowledge necessary for operating machines. Knowledge that they cannot acquire directly is acquired in indirect ways. The purpose of intelligence machines is applying to machines experts' knowledge acquisition process and their skills in operating machine. An agent is an autonomous process that recognizes external environment, exchanges knowledge with external machines and performs an autonomous decision-making function in order to achieve common goals. This paper describes agent application for intelligence machine.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.86-93
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• 2012

This paper presents a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of eight agents works together instantaneously to find suitable moving directions and follow the univector field to pursue and capture a prey agent by surrounding it from eight directions in an infinite grid-world. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
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• pp.67-68
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• 2023

본 논문에서 제안한 Multi Agent Multi Action은 기존의 대화형 시스템 방식인 Single Agent Single Action 구조에 비해 확장성을 갖춘 대화 시스템을 구현하는 방식이다. 시스템을 여러 에이전트로 분할하고, 각 에이전트가 특정 액션에 대한 처리를 담당함으로써 보다 유연하고 효율적인 대화형 시스템을 구현할 수 있으며, 다양한 작업에 특화된 에이전트를 그룹화함으로써 작업의 효율성을 극대화하고, 사용자 경험을 향상 시킬 수 있다.

• 한국IT서비스학회지
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• 제15권4호
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• 한국항공운항학회지
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• 제30권2호
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• pp.34-43
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• 2022

In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.

• 산업경영시스템학회지
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• 제29권1호
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• pp.87-93
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• 2006

In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. The purpose of this paper is to present the design of Decision Support Agent that is applicable to machine tools. This system is that decision whether to act in accordance with machine status is support system. It communicates with other active agents such as sensory and dialogue agent. The proposed design of decision support agent facilitates the effective operation and control of machine tools and provides a systematic way to integrate the expert's knowledge that will implement Intelligent Machine Tools.

• 한국게임학회 논문지
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• 제20권6호
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• pp.63-74
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• 2020

최근, 사람 대신 인공지능 에이전트를 이용하여 게임 테스트를 자동화하는 연구가 관심을 모으고 있다. 본 논문은 게임 밸런싱 자동화를 위한 선행 연구로써 사람과 인공지능으로부터 플레이 데이터를 수집하고 이들의 유사도를 분석하고자 한다. 이때, 사람과 유사한 플레이를 할 수 있는 인공지능의 생성을 위해 학습 단계에서 제약사항을 추가하였다. 플레이 데이터는 14명의 사람과 60개의 인공지능을 대상으로 플리피버드 게임을 각각 10회 실시하여 획득하였다. 수집한 데이터는 코사인 유사도 방법으로 이동 궤적, 액션 위치, 죽은 위치를 비교 분석하였다. 분석 결과 사람과의 유사도가 0.9 이상인 인공지능 에이전트를 찾을 수 있었다.

• 한국콘텐츠학회논문지
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• 제10권10호
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• pp.86-93
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• 2010

스마트폰의 도입으로 인하여 증강현실이 널리 알려짐에 따라 대중들의 관심은 이에 집중되고 있으며 휴대성으로 인하여 모바일기기에서의 증강현실 연구가 하나의 흐름으로 자리 잡고 있다. 기존의 증강 현실 관련 응용 기술들이 많이 연구되고 있지만 실제 게임에서 사용되고 있는 인공 지능과 결합된 연구는 이루어지고 있지 않다. 본 논문에서는 스마트 폰의 기능중 하나인 3축 가속도 센서를 이용하여 증강 현실 환경에서 3D 에이전트의 상태를 동적으로 변환하는 인공 지능 알고리즘을 제안한다. 인공지능이 적용된 에이전트의 상태를 제어하기 위한 전통적인 방식은 사용자가 직접 입력해 주거나 이를 인식하는데 마커를 사용하여 해결하였다. 정확한 상태 변환을 위하여 가속도 센서의 값에 임계값을 주었으며 임계값은 실험을 통하여 결정되었다. 본 논문에서는 증강 현실 구현을 위해 마커리스 추적 기술을 사용하였고 3축 가속도 센서를 이용하여 동적으로 에이전트의 상태를 변환하도록 하였다.

• International Journal of Advanced Culture Technology
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• 제8권1호
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• pp.165-172
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• 2020

Industrial Revolution means the improvement of productivity through technological innovation and has been a driving force of the whole change of economic system and social structure as the characteristic of technology as the tool of this productivity has changed. Since the first industrial revolution of the 18th century, productivity efficiency has been advanced through three industrial revolutions so far, and this fourth industrial revolution is expected to bring about another revolution of production. In this study, the demand for the introduction of artificial intelligence(AI) technology has been increasing in various business fields due to the rapid development of ICT technology, and the classification of HS(harmonized commodity description and coding system) items has been decided using artificial intelligence technology, which is the core of the fourth industrial revolution. And it is enough to construct HS classification system based on AI technology using inference and deep learning. Performing the HS item classification is not an easy task. Implementation of item classification system using artificial intelligence technology to analyze information of HS item classification which is performed manually by the current person more accurately and without any mistake, And the customs administrations, customs offices, and customs agencies, it is expected to be highly utilized in the innovation of trade practice and the customs administration innovation FTA origin agent.