• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.45-49
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• 2006

홈네트워크 시스템의 본격적인 보급과 함께 가정용서비스 로봇의 최근 연구 성과들은 인간과 지능로봇이 가정에 공존하며 서로 의사소통을 할 수 있는 시대가 가까운 미래에 현실화 될 수 있음을 보여주고 있다. 그러나 가정의 환경적인 특징은 open되어 있기 때문에 그러한 환경에 적응하고 주어진 임무를 수행하는 데는 단일 로봇 또는 단일 홈서버 보다는 로봇을 포함하는 홈네트워크 시스템 내의 여러 장치들이 어울려 분산처리를 수행하는 multi-agent 시스템이 일반적으로 더 좋다고 알려져 있다. 따라서 본 논문은 홈네트워크 시스템 환경에서 가정에서 필요한 agent들을 정의하기 위한 framework 모델을 구축하고 각 agent 간의 통신 protocol architecture를 제시한다. 또한 로봇 또는 홈서버의 단일 지능이나 기능보다는 그 안에 존재하는 복수개의 agent instance들의 집합으로 agent를 정의하고 각 agent 내외에서 agent들 사이의 협력(cooperation)과 (타협)negotiation을 통해 환경과 적응하는 방법 및 사람과 교감(interactive)하는 방법을 제시한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.17-21
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• 2007

Multi-Agent Robotic System (MARS) is a system that independent autonomous robots in the restricted environments infer their status from pre-assigned conditions and operate their jobs through the cooperation with each other. In the MARS, a robot contains sensor part to percept the situation around themselves, communication part to exchange information, and actuator part to do given work. Especially, in order to cooperate with other robots, communicating with other robots is one of the essential elements. Because Bluetooth has many advantages such as low power consumption, small size module package, and various standard protocols, Bluetooth is rated as one of the efficient communicating technologies which can apply to small-sized robot system. In this paper, we will develop Bluetooth communicating system for autonomous robots. For the purpose, the communication system must have several features-separated module, flexible interface. We will discuss how to construct and what kind of procedure to develop the communicating system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.292-297
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• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권1호
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• pp.1-11
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• 2015

A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.612-616
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• 1997

In this paper, two kinds of controller are proposed for a soccer robot system.. One for Supervisor and defense mode, and the other for attack mode. Robot soccer game has very dynamic characteristics. Furthermore, there exist competitions between agents. The soccer-playing robot should take an appropriate action according to its surroundings. Initially, an attack mode controller using a vector field concept is designed, then a supervisor and a defense mode controller are designed with a Petri-net. The efficiency and applicability of the proposed controllers are demonstrated through a real robot soccer game(MiroSot 97).

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• 로봇학회논문지
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• 제2권3호
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• pp.234-241
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• 2007

Effective tools which can alleviate the complexity and computational load problem in collision-free motion planning for multi-agent system have steadily been demanded in robotics field. To reduce the complexity, the extended collision map (ECM) which adopts decoupled approach and prioritization is already proposed. In ECM, the collision regions which represent the potential collision of robots are calculated using the computational power; the complexity problem is not resolved completely. In this paper, we propose a mathematical analysis of the extended collision map; as a result, we formulate the collision region as an equation with 5-8 variables. For mathematical analysis, we introduce realistic assumptions as follows; the path of each robot can be approximated to a straight line or an arc and every robot moves with uniform velocity or constant acceleration near the intersection between paths. Our result reduces the computational complexity in comparison with the previous result without losing optimality, because we use simple but exact equations of the collision regions. This result can be widely applicable to coordinated multi-agent motion planning.

• 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.

• 한국통신학회논문지
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• 제27권3C호
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• pp.226-233
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• 2002

This paper proposes the web robot based on Multi-Agent which the mutual dependency should be minimized each other with dividing the function each to collect Webpage. In result it is written to make a foundation for producing the effective statistics to analyze the domestic webpages and text, multimedia file composition ratio through performance analysis of the implemented system. It is easy that Web robot of the sequential processing method to collect Webpage on the same resource environment produces the limit of collecting performance. So to speak Webpages have "Dead-links" URL which is produced by the temporary host down and instability of network resource. If there are much "Dead-links" URL in the webpages, it takes a lot of time for web robot to collect HTML. The propose of this paper to be proposed, makes the maximum improvement to extract the webpages to process "Dead-links" URL on the Inactive URL scanner Agent.