• Journal of Korea Game Society
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• v.2 no.2
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• pp.37-45
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• 2002

Computer games could be regarded as simulation of the real world. Control problems of software agents have long been studied in the field of Artificial Intelligence (AI), resulting in giving a birth to the behavior-based approach. three main approaches might be categorized out of the history of AI study. First, Cognitivists propose that intelligence could be represented and manipulated in terms of symbols. Second, Connectionists claim that symbols could not be isolated but they are embedded in the body structure. Third, the behavior-based approach is an approach to AI which suggests that intelligence is dynamic property that exists nowhere but emerges in the relationship of an agent and the world including observers while the agent performs behavior. This paper explains and compares the three approaches to AI, then discusses the plausibility of the behavior-based approach and problems. Finally, this paper proposes application of behavior-based approach to computer games in terms of agent control.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.591-601
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• 2024

As generative AI-based educational content and services targeting child users rapidly increase, the need for research related to children's perception of generative AI is increasing. Accordingly, this study sought to determine the type of generative AI recognized by children and whether cognitive, behavioral, and emotional properties were assigned to it. To understand this, we collected responses through workshop activities to create storybooks with children, semi-structured interviews, and drawing. As a result, children viewed generative AI as an artifact with a high cognitive level, but it was not a type of existing artifact.

• Smart Media Journal
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• v.13 no.5
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• pp.19-25
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• 2024

The biggest type of behavior that prevents people with developmental disabilities from entering society is aggressive behavior. Aggressive behavior can pose a threat not only to the personal safety of the person with a developmental disability, but also to the physical safety of others. In this study, we propose a wearable system using a low-power processor. The proposed system uses an IMU (Inertial Measurement Unit) to analyze user behavior, and when attack behavior is not detected for a certain period of time through an LED array attached to the developed system, an interesting LED is displayed. By expressing patterns, we provide behavioral intervention through compensation to people with developmental disabilities. In order to implement a system that must be worn for a long time in a power-limited environment, we present a method to optimize performance and energy consumption across all stages, from data preprocessing to AI model application.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.253-257
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• 2023

최근 다양한 대화 시스템이 스마트폰 어시스턴트, 자동 차 내비게이션, 음성 제어 스피커, 인간 중심 로봇 등의 실세계 인간-기계 인터페이스에 적용되고 있다. 하지만 대부분의 대화 시스템은 텍스트 기반으로 작동해 다중 모달리티 입력을 처리할 수 없다. 이 문제를 해결하기 위해서는 비디오와 같은 다중 모달리티 장면 인식을 통합한 대화 시스템이 필요하다. 기존의 비디오 기반 대화 시스템은 주로 시각, 이미지, 오디오 등의 다양한 자질을 합성하거나 사전 학습을 통해 이미지와 텍스트를 잘 정렬하는 데에만 집중하여 중요한 행동 단서와 소리 단서를 놓치고 있다는 한계가 존재한다. 본 논문은 이미지-텍스트 정렬의 사전학습 임베딩과 행동 단서, 소리 단서를 활용해 비디오 기반 대화 시스템을 개선한다. 제안한 모델은 텍스트와 이미지, 그리고 오디오 임베딩을 인코딩하고, 이를 바탕으로 관련 프레임과 행동 단서를 추출하여 발화를 생성하는 과정을 거친다. AVSD 데이터셋에서의 실험 결과, 제안한 모델이 기존의 모델보다 높은 성능을 보였으며, 대표적인 이미지-텍스트 자질들을 비디오 기반 대화시스템에서 비교 분석하였다.

• Annual Conference of KIPS
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• 2022.05a
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• pp.618-620
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• 2022

게임은 수많은 NPC 와 규칙에 의해 작동되는 가상 공간을 의미한다. 이런 가상 공간에서는 규칙을 엄격히 지키면서 수행되는 AI 를 필수로 요구하게 된다. 하지만 강화 학습 기반의 AI 는 복잡한 게임의 규칙을 온전히 지키지 못하고 예상 밖의 행동을 돌출하면서 이를 해결하기 위한 많은 연구도 수행되고 있다. 본 논문에서는 규칙 기반으로 획득한 오목판의 확률 맵과 학습을 통해 획득한 확률맵 데이터를 병합하여 가장 높은 Value 를 가지는 위치를 다음 수로 반환하는 방법을 사용하였다. 향후 연구에서는 ANN(Approximate Nearest Neighbor)알고리즘을 적극 활용하여, 커널의 State 와 보드의 State 비교를 확률적으로 개선할 예정이다. 본 논문에서 제안된 프레임 워크는 게임 AI 연구에 기여할 수 있길 바란다.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1073-1082
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• 2004

This paper presents a technique for an agent to adaptively behave to unforeseen and dynamic circumstances. Since the traditional methods utilized the information about an environment to control intelligent agents, they were robust but could not behave adaptively in a complex and dynamic world. A behavior-based method is suitable for generating adaptive behaviors within environments, but it is necessary to devise a hybrid control architecture that incorporates the capabilities of inference, learning and planning for high-level abstract behaviors. This Paper proposes a 2-level control architecture for generating adaptive behaviors to perceive and avoid dynamic moving obstacles as well as static obstacles. The first level is behavior-network for generating reflexive and autonomous behaviors, and the second level is to infer dynamic situation of agents. Through simulation, it has been confirmed that the agent reaches a goal point while avoiding static and moving obstacles with the proposed method.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.3
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• pp.114-124
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• 2015

The concepts of activity involvement(AI) and place attachment(PA) are useful for explaining the sustainable use of natural resources by humans. Although several studies have investigated the effects of AI and PA on environmental behaviors and found its implications, it has not examined the simultaneous effects of both AI and PA. Thus, the purpose of this study was to develop a typology of both AI and PA. This typology was used to explain the environmentally responsible behaviors of visitors. The study sample surveyed 587 users of the main trail in Namhansanseong Provincial Park The results were analyzed by frequency, reliability, factor analysis, cross-tabulation, T-test, correlation and ANOVA analysis. As a result, the typology identified four subgroups of hikers based on involvement in hiking and attachment to setting. Results also indicate that environmentally responsible behaviors do vary significantly across typology. In detail, general environmental behavior and specific environmental behavior were significantly different between the four groups. These finding suggests that PA seems to play a more powerful role than AI in relation to environmental behavior. While more involved and more attached hikers were more active in environmental behaviors, less involved and less attached hikers had a more passive attitude. In this respect, this study placed emphasis on the fact that the future resource management of tourism and outdoor recreation may be established based on its activity experience in certain place.

• 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.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.405-412
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• 2021

To prevent problems such as portrait rights, copyright, and cyber violence, an ethics education STEAM projects using deepfake apps using AI technology were developed and applied. The Deepfake apps were screened, and the contents of the elementary school curriculum were reconstructed. The STEAM project as creative experiential activities was mainly operated by the UCC activities, and applied the info-ethics awareness measurement test based on the planned behavior theory. The social STEAM project as money (financial) education was qualitatively analyzed. It was found that this STEAM classes using AI technology app significantly enhances the ethical awareness of information communication.