• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.205-210
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• 2022

Drones, which have rapidly grown along with the 4th industrial revolution, spread over industries and also widely used for military purposes. In recent wars in Europe, drones are being evaluated as a game changer on the battlefield, and their importance for military use is being highlighted. The Republic of Korea Army also planned drone-bot systems including various drones suitable for echelons and missions of the military as future defense forces. The keyword of these drone-bot systems is autonomy by artificial intelligence. In addition, common use of operating platforms is required for the rapid development of various types of drones. In this paper, we propose software framework that applies diverse artificial intelligence technologies such as multi-agent system, cognitive architecture and knowledge-based context reasoning for mission autonomy and common use of military drones.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.932-935
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• 2017

최근 인공지능은 딥러닝, 기계학습 등 인공지능 기술이 발전되면서 기술 상용화가 가시화되고 있다. 이에 따라 인공지능분야는 다른 산업의 핵심 기술로 급부상과 함께 여러 글로벌 기업들이 적극적 투자를 실시하고 있는 추세이다. 이렇게 인공지능 기술이 발전하면서 인공지능 기반 기술 개발에서 타산업의 핵심기술로 프레임이 변화 되고 있으며 차세대 ICT 핵심 기술로 인식이 확산되고 있다. 따라서 본 논문에서는 이러한 인공지능 방법중 지도 학습의 의사 결정 트리 알고리즘을 사용하여 AWS(Amazone Web Service) EMR 서버에서 이를 알까기에 적용하여 알까고 게임 시스템을 구현하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.89-92
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• 2004

로보코드는 사용자가 직접 제작할 수 있는 슈팅게임 환경으로서, 이를 이용한 경진대회가 개최되고 있다. 매우 다양한 작전을 구사하는 로봇들이 인터넷을 통해 공개되지만, 대부분의 전략은 사람이 직접 설계하여 행동이 단순하고, 변화하는 환경에 따라 행동을 구사하는데에 어려움을 가지고 있다. 이로 인해 아무리 훌륭한 전략을 가지고 있더라도 환경적 요소에 따라 예상치 못한 이벤트가 발생했을 경우 적절한 행동을 선택하여 행하기가 어렵다. 본 논문에서는 동적인 환경에서 적절한 행동을 선택하는 행동선택 네트워크를 이용하여 상대 전략에 따라 적절한 행동을 선택하는 방법을 제안하고 로보코드에 적용하여 실험하였다. 실험결과, 상대 탱크의 전략에 따라 다양한 행동들을 자동으로 선택하였으며, 경기 결과로 그 전략의 우수성이 입증되었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.170-177
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• 2010

Poker is a game of imperfect information where competing players must deal with multiple risk factors stemming from unknown information while making the best decision to win, and this makes it an interesting test-bed for artificial intelligence research. This paper introduces a new learning AI algorithm with embedded opponent modeling that can be used for these types of situations and we use this AI and apply it to a poker program. The new AI will be based on several graphs with each of its nodes representing inputs, and the algorithm will learn the optimal decision to make by updating the weight of the edges connecting these nodes and returning a probability for each action the graphs represent.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.243-246
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• 2018

본 논문에서는 2차원 공간상에서 상부에서 하부로 떨어지는 블록을 하부에서 피하는 게임에서 강화 학습에 사용되는 DQN 알고리즘을 이용하여 블록 피하기 에이전트를 구현하고 학습 통해 점점 더 높은 점수를 받는 모습을 확인하였다. 파이썬을 이용하여 게임을 구현한 후 텐서플로우를 이용하여 DQN를 이용한 에이전트를 구현하였다. 에이전트는 보상을 통한 학습을 통하여 점점 강화되도록 하였는데, 초기에는 무작위로 움직였으나, 환경으로부터 받는 보상으로 점점 더 능숙하게 피하는 모습을 관찰할 수 있었다. 본 구현에서는 4000번 정도의 게임 시행에서 아주 능숙하게 피하는 결과를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.593-600
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• 2021

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.85-88
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• 2023

미국 콜로라도주 박람회 미술전에서 신인 디지털 아티스트 부문에서 1위를 차지한 게임 디자이너인 제이슨 앨런의 작품 스페이스오페라 극장'이 AI Image generator Midjourney를 활용해서 완성된 작품이라는 것이 알려지면서 창작과 예술 분야에 AI 활용이라는 논쟁이 가속화되고 있다. 창작과 예술을 돕는 탁월한 기능을 가진 툴로 바라보거나 창작과 예술 활동에 아이디어를 제공하고 작품을 구체화하는 과정의 조력자로 환영하는 입장과 예술가의 작품을 허가 없이 훔쳐서 만들어 낸 이미지일 뿐이라는 이상도 이하도 아니며 도덕적으로 허락되어서는 안되다는 입장이 크게 충돌하고 있다. 하루가 다르게 빠르게 발전하고 있는 주요 AI Image generator를 살펴보고 창작과 예술 분야에 AI 활용은 어떤 변화를 가져올지, AI 활용의 긍정적인 측면을 예측하고 연구해 보고자 한다.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.66-75
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• 2010

This paper proposes an efficient method for design and implementation of the artificial intelligence (AI) of 'omok' game on the GPU. The proposed AI is designed on a cooperative structure using min-max game tree and genetic algorithm. Since the evaluation function needs intensive computation but is independently performed on a lot of candidates in the solution space, it is computed on the GPU in a massive parallel way. The implementation on NVIDIA CUDA and the experimental results show that it outperforms significantly over the CPU, in which parallel game tree and genetic algorithm on the GPU runs more than 400 times and 300 times faster than on the CPU. In the proposed cooperative AI, selective search using genetic algorithm is performed subsequently after the full search using game tree to search the solution space more efficiently as well as to avoid the thread overflow. Experimental results show that the proposed algorithm enhances the AI significantly and makes it run within the time limit given by the game's rule.