• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.925-926
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• 2006

In the multi-agent system for a single task, the action selection can be made for the real-time environment by using the global coordination space, global coordination graph and MAX-PLUS algorithm. However, there are some difficulties in multi-agent system for multi-tasking. In this paper, a real-time decision making method is suggested by using coordination space, coordination graph and dynamic coordinated state of multi-agent system including many agents and multiple tasks. Specifically, we propose locally dynamic coordinated state to effectively use MAX-PLUS algorithm for multiple tasks completion. Our technique is shown to be valid in the box pushing simulation of a multi-agent system.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.190-194
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• 1998

In this paper, we introduce the micro robot soccer playing system as a standard test bench for the study on the multiple agent system. Our method is based on following viewpoints. They are (1) any complex behavior such as cooperation among agents must be completed by sequential basic actions of concerned agents. (2) those basic actions can be well defined, but (3) how to organize those actions in current time point so as to result in a new stale beneficial to the end aim ought to be achieved by a kind of self-learning self-organization strategy.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• 전기학회논문지
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• 제56권12호
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• pp.2072-2077
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• 2007

The primary objective of all power systems is to maintain the reliability and to minimize outage time for fault or the others. The frequency relaying algorithm perceives a variation of system frequency and thereafter detects the unbalance between generation and load. A multi-agent system is composed of multiple interacting computing elements that are known as agents. In this paper, frequency relaying algorithm is designed by multi-agent system and is implemented by EMTP-MODELS. To verify performance of the frequency relaying algorithm based on multi-agent system, simulations by EMTP have been carried out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2050-2077
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• 2015

One of the major issues in emerging cloud management system needs the efficient service level agreement negotiation framework, with an optimal negotiation strategy. Most researchers focus mainly on the atomic service negotiation model, with the assistance of the Agent Controller in the broker part to reduce the total negotiation time, and communication overhead to some extent. This research focuses mainly on composite service negotiation, to further minimize both the total negotiation time and communication overhead through the pre-request optimization of broker strategy. The main objective of this research work is to introduce an Automated Dynamic Service Level Agreement Negotiation Framework (ADSLANF), which consists of an Intelligent Third-party Broker for composite service negotiation between the consumer and the service provider. A broker consists of an Intelligent Third-party Broker Agent, Agent Controller and Additional Agent Controller for managing and controlling its negotiation strategy. The Intelligent third-party broker agent manages the composite service by assigning its atomic services to multiple Agent Controllers. Using the Additional Agent Controllers, the Agent Controllers manage the concurrent negotiation with multiple service providers. In this process, the total negotiation time value is reduced partially. Further, the negotiation strategy is optimized in two stages, viz., Classified Similarity Matching (CSM) approach, and the Truncated Negotiation Group Gale Shapely Stable Matching (TNGGSSM) approach, to minimize the communication overhead.

• 지능정보연구
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• 제8권2호
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• pp.205-227
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• 2002

이 연구는 웹 기반 협동작업 시스템에서 지능적인 에이전트들을 이용하여 원격 회의의 소집을 자동화함으로써 여러 참여자들에게 보다 편리한 형태의 협동작업 환경을 지원해 주는 시스템에 대한 것이다 회의 일정을 잡는 작업을 자동화하는 것은 각 개인의 공적인 일정 뿐만 아니라 개인의 사정과 선호도 등에 대한 세심한 배려를 요구한다. 그러므로, 회의 소집 자동화는 신뢰성과 병렬성을 높이기 위해서 각 개인의 일정을 병렬적으로 별도 관리하는 작업을 지원하는 분산 처리 작업이 요구된다. 이 논문에서는 회의 소집에 들이는 시간과 노력을 최소로 줄여주는 다중 에이전트 시스템의 설계와 구현에 대하여 자세하게 소개한다. 이 시스템은 서버-를라이언트 모델에 기반하며, 서버 측에서는 스케줄 에이전트, 협상 에이전트, 개인정보 관리 에이전트, 그룹 정보 관리 에이전트, 세션관리 에이전트와 조정 에이전트가 활동한다. 클라이언트 측에서는 인터페이스 에이전트, 미디어 에이전트와 협동 에이전트가 활동한다. 이들 에이전트들은 통신은 표준화된 지식 표현 언어를 이용하여 통신하므로 분산 협동 처리를 위한 에이전트들 간의 통신에 있어 가장 큰 문제점인 이형질성을 극복할 수 있도록 해준다. 본 시스템의 회의소집 지원 에이전트들은 전진연결 알고리즘으로 추론하고 역전파 네트워크 알고리즘으로 학습하여 가장 많은 사람이 참여할 수 있는 날짜를 제안해줌으로써 회의 소집자가 회의 소집에 들이는 노력을 최소로 줄일 수 있게 해 준다.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.636-640
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• 2002

In contrast to conventional CNC, PC-NC opens a new era for machine tools to be more intelligent. For instance, machine tool with PC-NC can be a machine agent system with capability of reacting autonomously to changing operating conditions. This paper introduces a concept of intelligent machine agent system, composed of machine agent and cell manager. Machine agent performs the functions such as process monitoring, diagnosis, maintenance management, condition assessment and schedule negotiation, while cell manager coordinates the negotiation process among multiple machine agents.