• Proceedings of the Korean Information Science Society Conference
• /
• 2000.04b
• /
• pp.304-306
• /
• 2000

다양한 문제의 해결을 위하여 요구되는 각종 지식의 처리는 인공 지능 분야의 중요한 주제가 되어 왔다. 본 논문에서는 여러 종류의 지식중에서 많이 연구되어 있지 않은 삽화 지식의 획득과 저장에 관하여 논의하며, 삽화 지식은 플랜 인지의 과정을 통해 알아낸 목표의 연속체로서 자동 에이전트의 영구 기억에 저장되어야 한다고 제안한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.3
• /
• pp.347-353
• /
• 2008

The realization of intelligence by using ontology is getting attention recently. However, it is necessary to design ontology models suitable to their purpose in order to use efficiently the intelligence realized by ontology. In this paper, we define a cognition cycle for intelligent agents representing a process that the intelligent agents recognize an event and react to it. Moreover, we design an ontology-based intelligent agent, and propose an ontology model that is possible to change the agent's states, to express its emotions, and to expand its intelligence through ontological inference. Finally, we develop an intelligent agent named Helen, confirm the change of her inner states according to the environment and situation, and show the easiness of the extension of her intelligence.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.205-207
• /
• 2006

이질적이고 분산된 환경에서의 컴퓨팅 요소들은 변화하는 사용자의 요구와 상황을 인지하여 주위 환경에 적응할 필요가 있으며 이렇게 자동화된 요소, 즉 적응된 에이전트들은 동적으로 설정된 목표를 해결하기 위해 서로 협력해야 한다. 이렇게 적응된 에이전트를 구현하기 위해서는 이들이 인지된 상황을 추론할 수 있는 지식베이스의 구축이 필수적이며 이러한 지식을 표현할 수 있는 모델이 필요하다. 본고에서는 다중 에이전트 시스템에서 지적 활동의 중심적 자료구조가 될 온톨로지와 온톨로지를 효과적으로 관리하는 온톨로지 저장소를 활용하여 다양한 지식원과 에이전트간의 의미적 상호작용을 증대시키기 위한 Knowledge Description Model을 제시한다.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.4
• /
• pp.1-10
• /
• 2010

This present paper's goal is to show a virtual human agent's movement-data loci based on realistically limited perception data is human-like. To determine human-likeness of the movement-data loci, we consider interactions between two parameters: Realistically Limited Perception (RLP) data and Incremental Movement-Path data Generation (IMPG). That is to consider how the former (i.e., RLP), one of the simulated parameters of human thought or its elements dictates the latter (i.e., IMPG), one of the simulated parameters of human movement behavior. Mapping DB is a prerequisite for navigation in an agent system because it functions as an interface between perception and movement behavior. Although Hill et al. studied mapping DB methodology based on RLP, their research dealt only with a rendering camera's view point data. The agent system in this present paper was integrated with the Hill's mapping DB module and then the two parameters' interaction was considered on a military reconnaissance mission with unexpected enemy emergence. Movement loci that were generated by the agent and subjects were compared with each other. The agent system in this present research verifies that it can be a functional test bed for producing human-like movement-data loci although the human-likeness of agent is the result of a pilot test, determined by two parameters (RLP and IMPG) and only 30 subjects.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.3
• /
• pp.51-58
• /
• 2010

Since Augmented Reality (AR) enables users to experience computer-generated content embedded in real environments, AR agents can be visualized among physical objects in the environments where the users exist, and directly interact with them in real-time. We model perceptual attention for autonomous agents in AR environments where virtual and physical objects coexist. Since such AR agents must adaptively perceive and attend to surrounded objects relevant to their goals, our model allows the agents to determine currently visible objects from the description of what virtual and physical objects are configured in the camera's viewing area. A degree of attention is assigned to each perceived object based on its relevance to achieve agents' goals. The agents can focus on a reduced set of perceived objects with respect to the estimated degree of attention. To demonstrate the effectiveness of our approach, we implemented an animated character that was overlaid over a miniature version of campus and that attended to buildings relevant to their goals. Experiments showed that our model could reduce the character's perceptual loads even when surroundings change.

• Korean Journal of Cognitive Science
• /
• v.10 no.1
• /
• pp.1-11
• /
• 1999

Agent-based systems technology has generated lots of excitement in these years because of its promise as a new paradigm for conceptualizing. designing. and l implementing software systems Especially, there has been many researches for multi agent system because of the characteristics that it fits to the distributed and open Internet environments. In a multiagent system. agents must cooperate with each other through a Coordination procedure. when the conflicts between agents arise. where those are caused b by the point that each action acts for a purpose separately without coordination. But P previous researches for coordination methods in multi agent system have a deficiency that they can not solve correctly the cooperation problem between agents which have different goals in dynamic environment. In this paper. we solve the cooperation problem of multiagent that has multiple goals in a dynamic environment. with an automatic cooperative coordination model using I reinforcement learning. We will show the two pursuit problems that we extend a traditional problem in multi agent systems area for modeling the restriction in the multiple goals in a dynamic environment. and we have verified the validity of the proposed model with an experiment.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.5
• /
• pp.63-73
• /
• 2008

Recent years have seen an explosion of interest in interactive agents motivating human learners to engage in edutainment systems which are designed to be entertaining and educational at the same time. Especially, work on socio-emotional processes has focus on understanding of human's social behavior in training and entertainment a applications. In contrast with work on social emotion, where research groups have developed detailed models of emotional processes, models of personality have emphasized shallow surface behavior. Here, we build on computational appraisal models of emotion to better characterize dispositional differences in how people come to understand social situations. Known as explanatory style, this dispositional factor plays a key role in social interactions and certain socio-emotional disorders, such as depression. Building on appraisal and attribution theories, we model key conceptual variables underlying the explanatory style, and enable agents to exhibit different explanatory tendencies with respect to their personalities. Furthermore, we developed an interactive AR agent based on our framework and applied it into an interactive teaming system that allows participants to explore individual differences in the explanation of social events, with the goal of encouraging the development of perspective laking and emotion-regulatory skills.

• Korean Journal of Cognitive Science
• /
• v.11 no.1
• /
• pp.67-80
• /
• 2000

Distributed cooperative information system is the one that has various knowledge sources as well as problem solving capabilities to get information in a distributed and heterogeneous data environment. In a distributed cooperative information system. a control mechanism to facilitate the available information is very important. and usually the role of the control mechanism determines the behavior of the total system In this research. we proposed a model of the distributed cooperative information system which is based on the multi-agent paradigm. We also implemented a test system to show l its feasibility. The proposed system makes the knowledge sources into agents and a special agent called 'facilitator' controls the cooperation between the knowledge agents The facilitator uses the knowledge granularity level to determine the sequence of the activation of the agents. In other words. the knowledge source with simple but fast processing mechanism activates first while more sophisticated but slow knowledge sources are activated late. In an environment in which we have several knowledge sources for the same topic. the proposed system will simulate the focusing mechanism of human cognitive process.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10a
• /
• pp.46-48
• /
• 2003

본 논문에서는 실제환경에서와 같이 예측할 수 없는 상황에서 에이전트의 인지와 자동 행동 생성 방법을 제안한다. 전통적인 에이전트의 지능제어 방법은 환경에 대해 알고 있는 정보를 이용한다는 제약 때문에 다양하고 복잡한 환경에 적응할 수 없었다. 최근, 미리 알려지지 않은 환경에서 자동으로 행동을 생성할 수 있는 센서와 행동을 연결하는 행동 기반의 방법과 추론, 학습 및 계획 기능의 부여를 위한 하이브리드 방법이 연구되고 있다. 본 논문에서는 다양한 환경조건으로 움직이는 장애물을 인지하고 피할 수 있는 행동을 생성하기 위해 행동 네트워크에 Bayesian 네트워크를 결합한 방법을 제안한다. 행동 네트워크는 입력된 센서 정보와 미리 정의된 목적 정보를 가지고 다음에 수행할 가장 높은 우선순위의 행동을 선택한다. 그리고 Bayesian 네트워크는 센서 정보들로부터 상황을 미리 추론하고 이 확률 값을 행동 네트워크의 가중치로 주어 행동 선택을 조정하도록 한다. 로봇 시뮬레이터를 이용한 실험을 통해 제안한 행동 네트워크와 Bayesian 네트워크의 결합 방법으로 움직이는 장애물을 피하고 목적지를 찾아가는 것을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.04b
• /
• pp.271-273
• /
• 2000

일반적으로, 지능형 에이전트는 사용자의 목적과 주위 환경으로부터 최적의 행동을 스스로 찾아낼 수 있어야 한다. 만약 에이전트의 목적이나 주위 환경이 불확실성을 포함하는 경우, 에이전트는 적절한 행동을 선택하기 어렵다. 그러나, 사용자의 목적이 인간 지식의 불확실성을 포함하는 언어값으로 표현되었을 경우, 이를 처리하려는 연구는 없었다. 본 논문에서는 모호한 사용자의 의도를 퍼지 목적으로 나타내고, 에이전트가 인지하는 불확실한 환경을 퍼지 상태로 표현하는 방법을 제안한다. 또, 퍼지 목적과 상태를 이용하여 확장한 펴지 강화 함수와를 이용하여, 기존 강화 학습 알고리즘 중 하나인 Q-Learning을 FuzzyQ-Learning으로 확장하고, 이에 대한 타당성을 검증한다.