서지주요정보
Implementation of distributed problem solving framework based on discrete event system specification = 이산사건 시스템 형식론에 기초한 분산문제해결 방법론의 구현
서명 / 저자 Implementation of distributed problem solving framework based on discrete event system specification = 이산사건 시스템 형식론에 기초한 분산문제해결 방법론의 구현 / Heung-Bum Kim.
발행사항 [대전 : 한국과학기술원, 1997].
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8007225

소장위치/청구기호

학술문화관(문화관) 보존서고

DEE 97028

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Distributed Problem Solving(DPS), as a part of Distributed Artificial Intelligence(DAI), deals with the interactions of agents attempting to solve problems together. The agent which is a problem solver has only partial information for global environment, and cannot solve a whole problem by itself. Many DPS systems have been developed using the concept from the distributed processing architecture and the AI rule representation. But a few systems take formalism-based approach. As the DPS maintains discrete event system property, we propose a DPS framework which is modeled by Discrete Event System Specification(DEVS) formalism and implemented in DEVSim++ which is a simulation tool dereloped in oure laboratory. The models of the framework classify both behavior and structure according to the character of the agent. Therefore, the operational meaning of an agent model can be exchanged by some representations. Our DPS system constructed flexibly our in modular, hierarchical manner and its model can be reused according to their applications. Using framework, we tested three manufacturing systems with respect to distributed control, failure/repair operation and a degree of redundancy. In the DPS, the knowledge source of the agent is an important element. The capability of problem-solving is determined by the knowledge source. Recently, neural network has been employed to the rule representation of knowledge sources. Back-propagation neural network is a kind of neural network. A significant problem is the training efficiency for acquiring knowledge source with the back-propagation neural network. In order to effectively acquire knowledge source, we suggest two training methods(on-line and window control) of the neural network. These methods are applied to evolutionary programming technique. The objective function and perturbation factor of evolutionary programming algorithm is modified by the parameters of the network. During two benchmark and one modulus problem tests, our methods were superior to otheradaptive training methods: quasi-newton, steepest-descent and weight-average methods. Specially, as the complexity of structure increases, our methods are much efficient. Merging these learning algorithms to our DPS frame work is remained further work.

서지기타정보

서지기타정보
청구기호 {DEE 97028
형태사항 ix, 131 p. : 삽도 ; 26 cm
언어 영어
일반주기 Appendix : A, Description of atomic models in DPS framework
저자명의 한글표기 : 김흥범
지도교수의 영문표기 : Kyu-Ho Park
지도교수의 한글표기 : 박규호
수록잡지명 : "Fast learning method for back-propagation neural network by evolutionary adaptation of learning rates". Neurocomputing. Elsevier Science B V, vol. 11, no. 1, pp. 101-106 (1996)
수록잡지명 : . IEEE International Conference on System, Man and Cybernetics. IEEE, pp. 4178-4183 (1995)
학위논문 학위논문(박사) - 한국과학기술원 : 전기및전자공학과,
서지주기 Reference : p. 126-131
주제 Distributed problem solving
Discrete event system
Manufacturing system
Back-propagation neural network
Evolutionary programming
분산문제해결
이산사건 시스템
제조 시스템
역전파 신경회로망
진화 프로그래밍
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