• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1124-1128
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• 2005

In this paper, a plan-coordination architecture is proposed for multi-agent control in the fractal manufacturing system (FrMS). A fractal in FrMS is a set of distributed agents whose goal can be achieved through cooperation, coordination, and negotiation with other agents. Since each agent in the FrMS generates, achieves, and modifies its own plan fragments autonomously during the coordination process with other agents, it is necessary to develop a systematic methodology for the achievement of global plan in the manufacturing system. The heterarchical structure of the FrMS provides a compromised plan-coordination approach, it compromise a centralized plan-generation/execution (which mainly focuses on the maximization of throughput) with a distributed one (which focuses on the autonomy of each module and flexibility of the whole system). Plan-coordinators in lower level fractal independently generate plan fragments according to the global plan of higher level fractal, and plan-coordinators in higher level fractal mediate/coordinate the plan fragments to enhance the global performance of the system. This paper assumes that generation method of the plan fragments and the negotiation policy of the fractal is achieved by a simple process, and we mainly focuses on the information exchanging and distributed decision making process to coordinate the combinations of plan fragments within a limited exchange of information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.201-205
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• 1997

As manufacturing system have become complex and globalized, rapid development and production of products are essentially requisites for competitiveness. The importance of agility in manufacturing is being emphasized and a new paradigm is necessary for reduction of the time and expenses related to planning, product development and production. To meet such requirements, virtual manufacturing (VM) environment was suggested. In this paper, Multi-agent system is adopted into VM operating system. Because our system is flexible due to agent technology, agents can be added or deleted with ease. VM unit modules which were defined as DEVS models execute independent simulation of other modules in unit level and compose one VM system with other modules. They also execute simulation in system level. This research can contribute to usefulness of VM environment due to flexibility and extensibility of this system.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• Asia pacific journal of information systems
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• v.10 no.2
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• pp.23-49
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• 2000

We proposed a conceptual design of the web-based agent model for global supply chain management(GSCM), where agents representing autonomous operational units, such as suppliers, factories, distribution center and customers, cooperate and are coordinated through the information exchange. The agent model assumed the hierarchical federated system. In the federated system, the agents of the same region are grouped and linked to the region-specific facilitator only through which communication between agents is allowed. The facilitator is responsible for monitoring and controlling the conversations consisting of the message flows across the agents. A web-based user presentation was also designed so that human users could involve in collaborative settings into the GSCM multi-agent system. In the conversation protocols which allow for complex coordinated behavior among agents, the KQML was extended to represent the messages. A GSCM scenario where the supply chain is formed upon customer order and supply decision is made was used to demonstrate the dynamics of the conversation protocols.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.242-248
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• 2004

Personalized reminder systems have to identify the user's current needs dynamically and proactively based on the user's current context. However, need identification methodologies and their feasible architectures for personalized reminder systems have so far been rare. Hence, this paper aims to propose a proactive need identification mechanism by applying agent and semantic web technologies and RFID-based context subsystem for a personalized reminder system, which is one of the supporting systems for a robust ubiquitous service support environment. We have created a prototype system, RFID-based NAMA (Need Aware Multi-Agent), to demonstrate the feasibility of the methodology and of the mobile settings framework that we propose in this paper. NAMA considers the context, user profile with preferences, and information about currently available services, to discover the user's current needs and then link the user to a set of services, which are implemented as web services.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.170-175
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• 1998

Transporting a large table using multiple robotic agents requires at least two group behaviors of homing and herding which are to bo coordinated in a proper sequence. Existing GP methods for multi-agent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we use a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to co-evolve more complex behavior. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behavior which may not be solved by simple genetic programming.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.270-276
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• 2012

With the increase in amount of data and information available on the web, there have been high demands on personalized information retrieval services to provide context-aware services for the web users. This paper proposes a novel dynamic multi-agent context-awareness user profile construction method based on ontology to incorporate concepts and properties to model the user profile. This method comprehensively considers the frequency and the specific of the concept in one document and its corresponding domain ontology to construct the user profile, based on which, a fuzzy c-means clustering method is adopted to cluster the user's interest domain, and a dynamic update policy is adopted to continuously consider the change of the users' interest. The simulation result shows that along with the gradual perfection of the our user profile, our proposed system is better than traditional semantic based retrieval system in terms of the Recall Ratio and Precision Ratio.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.247-250
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• 2006

급속도로 변화하는 환경에 적응하기 위해서 환경의 변화에 대한 요구와 신속한 응답능력을 향상시키고, 에이전트간 의사결정의 지속시간을 줄이기 위하여 에이전트간 효율적인 조정에 관련된 의사결정을 하기위한 대안(alternative)결정과 사용자의 선호도를 어떻게 유도할 수 있는가라는 문제가 요구된다. 본 논문에서는 사회적(Pareto) 최적성이라는 관점에서 의사결정의 행동을 효과적으로 시뮬레이트하기 우해 퍼지 의사결정에 기반한 멀티에이전트의 효율적인 조정방안을 제안한다. 또한 제안하는 방법에서는 가중치를 사용하여 각 속성이 멀티에이전트와 관련하여 최적의 대안을 생성하고, 퍼지 의사결정에 기반한 멀티에이전트의 의사결정방법에 기존의 방법보다 가중치를 사용한 방법이 높은 신뢰도를 가지면서 더 빠른 의사결정을 한다는 것을 확인하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.