• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.92-94
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• 2012

군수 시뮬레이션 환경에서 다중에이전트를 이용한 시뮬레이션 연구가 활발히 진행되고 있다. 본 논문은 전시상황에서 A* 알고리즘을 적용하여 에이전트간의 거리를 구하고, 유전알고리즘을 활용하여 제한된 물자를 효과적으로 에이전트들에게 분배하기 위한 모델링을 설계하고 구현하기 위한 방법을 제시하였으며, 시뮬레이션을 구현하였다.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.677-684
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• 2003

IETF's Mobile IP protocol allows Mobile Node(MN) to keep connection to Correspondent Node(CN) no matter what the MN changes its point of attachment to the Internet. However, the protocol incurs triangle routing problem, because all packets for the MN are routed through its Home Agent(HA). Route optimization mechanism is proposed by IETF in order to resolve triangle routing problem, but it requires that any node in the Internet should also implement route optimization mechanism. In this paper, we propose a new route optimization algorithm. With the algorithm, routers between HA and CN are considered as Intermediate Agent(IA), and packets transmitted by CN are not forwarded to HA but intercepted by IA and tunneled to MN directly.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.119-125
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• 2006

Distributed Systems can be defined as set of computing resources connected by computer network. One of the most significant techniques in optimization problem domains is parallel genetic algorithms, which are based on distributed systems. Since the status of dynamic network environments such as Internet and mobile computing. can be changed continually, it must not be efficient on the dynamic environments to solve an optimization problem using previous parallel genetic algorithms themselves. In this paper, we propose the effective technique, in which the parallel genetic algorithm can be used efficiently on the dynamic network environments.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.137-149
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• 2004

A new software paradigm is required on the development of network and various service requirements. With this, many studies on a mobile agent have been made. For the execution of the mobile agent, migration is the most important factor that influences the performance of the mobile agent. In this paper we propose the method that leads to high migration efficiency in order to improve the performance. The features of our migration technique are as follows. First, the migration technique creates the dynamic itinerary that appropriately copes with the network conditions and the platform changes to improve the agent execution efficiency. Second, it perfecters an executed code to reduce the amount of the mobile data and reduces the execution time by instantiating the agent in advance. Third, it improves the execution efficiency by using the checkpoint-based recovery method that does not execute the agent again and recovers the process states even though the errors take place. Though the simulation we compare the proposed method with the existing methods. The simulation result shows that the proposed method outperform the existing methods in terms of migration.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.111-116
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• 2000

경매는 인간에 의해 만들어진 복잡한 형태 시장이다. 전자상거래의 확산과 더불어, 많은 인터넷 경매 사이트가 나오고, 최근 경매의 자동화에 대한 연구가 진행 중에 있다. 인간 대신, 에이전트는 상품의 검색, 입찰등을 대신 수행하게 된다. 그러나, 현재의 입찰 알고리즘은 효율적이지 못하다. 본 연구는 통계적 방법을 이용하여 현재의 알고리즘보다 효율적인 알고리즘을 만들고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.237-240
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• 2004

공급망관리(SCM:Supply Chain Management)는 자신의 비즈니스를 개선하고자 노력하는 대부분의 기업들에게 있어서 핵심 목표가 되고 있다. 공급망최적화(SCO:Supply Chain Optimization)는 이러한 개선 노력의 중심 목표로서 등장했으며, 보다 높은 수준의 고객 만족을 제공하기 위해, 공급망 구성원들이 최상의 업무 수행 사례를 공유하여 공급망 상호작용의 최저 원가와 최고 효율 시스템을 개발하기 위한 조직화 된 노력을 의미한다. 이러한 공급망에서 성과를 내기 위해서는 각 구성 요소들 사이의 조정과 협력이 필요하다. 그러나 기업내부와 기업외부의 환경의 동적인 면으로 인해 이러한 조정에 어려움이 있다. 예를 들면 공급업체로부터 원자재 도착시간의 지연, 생산 설비의 고장, 작업자의 질병, 고객의 주문에 대한 취소 및 변경을 계획의 변화를 유발시킨다. 몇 가지의 경우에는 지엽적으로 다루어 질 수 있지만 대부분의 경우는 공급망의 여러 기능들 사이에서 다루어져야만 한다. 이에 따라 본 연구에서는 중소기업의 각 프로세스를 최적화시키기 위해 에이전트를 이용한 e-SCM 프레임워크를 구축하고, 시뮬레이션 기법을 통해 그에 따른 성과를 제시한다.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1367-1372
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• 2006

건물의 평면 디자인이나 도로의 디자인과 같은, 보행자를 고려해야 하는 디자인 혹은 그와 유사한 문제의 최적화를 위해서 군중 시뮬레이션이 필요한 경우가 있다. 이때 보행자 에이전트에 대한 모델이 필요하며 에이전트 디자인을 얼마나 신중하게 하느냐가 군중시뮬레이션의 결과에 많은 영향을 미친다. 기존의 보행자 모델은 에이전트로 하여금 이성적 행동을 하도록 하는 데에 초점이 맞추어져 있었다. 하지만 사람은 심리경향을 갖고 있으며, 이를 고려한 보행자 모델은 좀 더 현실과 가까운 군중 시뮬레이션이 가능하도록 해 준다. 본 논문에서는 MBTI(Myers-Briggs Type Indicator)의 척도 중 하나인 외향과 내향 척도를 기존의 보행자 모델에 적용시켰다. 외향과 내향을 하나의 변수로 보고 그에 따른 행동 패턴의 결정에 대한 함수를 만들었다. 그리고 이 모델을 사용하여 2 차원의 군중시뮬레이션을 해 보았다. 이 시뮬레이션은 출구를 가지고 있는 평면구조를 사용하여 위급한 상황일 때를 가정하여 에이전트들이 출구로 빠져나가도록 하는 것이다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.87-94
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• 2003

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. Ant Colony Optimization(ACO) is a new meta heuristic algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as Breedy search It was first Proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we deal with the performance improvement techniques through balance the Intensification and Diversification in Ant Colony System(ACS). First State Transition considering the number of times that agents visit about each edge makes agents search more variously and widen search area. After setting up criteria which divide elite tour that receive Positive Intensification about each tour, we propose a method to do addition Intensification by the criteria. Implemetation of the algorithm to solve TSP and the performance results under various conditions are conducted, and the comparision between the original An and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed to these problem.