In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a ...In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a less or rougher concept. With different translation sequences the amount of the missed knowledge is varied. The λ-optimal translation sequence of rough communication, which concerns both every agent and the last agent taking part in rough communication to get information as much as he （or she） can, is given. In order to get the λ-optimal translation sequence, a genetic algorithm is used. Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.展开更多
基金supported by the National Natural Science Foundation of China(61070241)the Natural Science Foundation of Shandong Province(ZR2010FM035)+1 种基金the Science and Technology Foundation of University of Jinan(XKY1031XKY0808)
文摘In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a less or rougher concept. With different translation sequences the amount of the missed knowledge is varied. The λ-optimal translation sequence of rough communication, which concerns both every agent and the last agent taking part in rough communication to get information as much as he （or she） can, is given. In order to get the λ-optimal translation sequence, a genetic algorithm is used. Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.
