• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.113-128
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• 2000

This paper presents a first year report of an ongoing multi-year project to develop a systolic parallel simulation system for dynamic traffic assignment. The fundamental approach to the simulation is systolic parallel processing based on autonomous agent modeling. Agents continuously act on their own initiatives and access to database to get the status of the simulation world. Various agents are defined in order to populate the simulation world. In particular existing modls and algorithm were incorporated in designing the behavior of relevant agents such as car-following model headway distribution Frank-Wolf algorithm and so on. Simulation is based on predetermined routes between centroids that are computed off-line by a conventional optimal path-finding algorithm. Iterating the cycles of optimization-then-simulation the proposed system will provide a realistic and valuable traffic assignment. Gangnum-Gu district in Seoul is selected for the target are for the modeling. It is expected that realtime traffic assignment services can be provided on the internet within 3 years.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.725-731
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• 2013

In This paper, through simulation, proposes a system which makes it possible for freely moving multiple agents with ultrasonic receivers to estimate their own position by themselves using the ultrasonic signals sequentially emitted from multiple ultrasonic transmitters. This system possesses an advantage that there is no limit in the number of agents existing in the space covered by more than three transmitters fixed at proper locations. Hence, the proposed system can be utilized effectively in position tracking control of multiple robotic agents system and motion capturing system.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.299-301
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• 2003

In this paper, a protection system using Multi-Agent concept for power distribution network is proposed. Multi agent system consist of Feeder agent, OCR(Over Current Relay) agent, Recloser agent and Switch agent. An agent calculates and corrects its parameter by itself through communication with neighboring agents and its own intelligence algorithm. Simulations in a simple distribution network show the effectiveness of the suggested protection system. Multi-Agent System, protection of distribution network, Communication.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.8
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• pp.365-372
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• 2001

This paper presents a new protection concept to achieve the adaptive optimal function by introducing the multi-agent system technology. Making each relay an agent which consists of relay agent module and coordinator module, each agent works autonomously to improve protection capability by cooperating with other agents. It is applied to the overcurrent protection system of the industrial power system in order to show the feasibility of the proposed methodology.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.8-13
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• 2008

This paper presents auto-reclosing algorithms with reference to power system stability based on MAS(Multi-Agent System). And this paper shows auto-reclosing algorithms considering power system stability. It includes the variable dead time, optimal reclosing, sequential reclosing, emergency extended equal-area criterion(EEEAC) algorithm, and modified EEEAC algorithm. This paper divides Auto-reclosing algorithms into respectively agents according to their tasks. A separated agent is merely a software entity that is situated in some environment and is able to autonomously react to changes in the environment. And all the simulations in this parer were tested by EMTP MODELS.

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

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.164.1-164
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• 2001

Q-learning is a recent reinforcement learning algorithm that does not need a modeling of environment and it is a suitable approach to learn behaviors for autonomous agents. But when it is applied to multi-agent learning with many I/O states, it is usually too complex and slow. To overcome this problem in the multi-agent learning system, we propose the successive Q-learning algorithm. Successive Q-learning algorithm divides state-action pairs, which agents can have, into several Q-functions, so it can reduce complexity and calculation amounts. This algorithm is suitable for multi-agent learning in a dynamically changing environment. The proposed successive Q-learning algorithm is applied to the prey-predator problem with the one-prey and two-predators, and its effectiveness is verified from the efficient avoidance ability of the prey agent.

• Journal of information and communication convergence engineering
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• v.20 no.3
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.232-235
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• 2003

In this paper, we implemented a FIPA-OS multi-agent framework bundle in OSGi Service Platform. FIPA-OS is an open agent platform for constructing FIPA compliant agent using mandatory components that required by all FIPA-OS agents to execution and optional components that FIPA-OS agent car optionally use. The platform supports communication between multiple agents and communication language which conforms to the FIPA standards. FIPA-OS framework bundle is composed of DE(Directory Facilitator), AMS(Agent Management System), ACC(Agent Communication Channel) and MTS(Message Transport System) bundle. These bundles installed in the OSGi service platform and their life cycle can be managed by the framework.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.29-33
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• 2004

I propose a personalized digital library system (PDLS) based on an advanced distributed agent platform. The new platform is developed by improving the DECAF (Distributed Environment-Centered Agent Framework) which is one of the conventional distributed agent development toolkits. Also, a mobile ORB (Object Request Broker), Voyager, and a new multi agent negotiation algorithm are adopted to develop the advanced platform. The new platform is for mobile multi agents as well as the distributed environment, whereas the DECAF is for the distributed and non-mobile environment. From the results of the simulation the searched time of PDLS is lower, as the numbers of servers and agents are increased. And the user satisfaction is four times greater than the conventional client-server model. Therefore, the new platform has some optimality and higher performance in the distributed mobile environment.