知识表示中的不确定性

Uncertainty in Knowledge Representation

知识表示一直是人工智能研究中的一个瓶颈 ,其难点在于知识中隐含有不确定性 ,即模糊性和随机性。文章提出用云模型 3个数字特征 (期望值 ,熵 ,超熵 )来描述一个定性概念 ,用熵来关联模糊性和随机性。代表定性概念的云的某一次定量值 ,被称为云滴 ,可以用它对此概念的贡献度来衡量 ,许许多多云滴构成云 ,实现定性和定量之间的随时转换 ,反映了知识表示中的不确定性。论文以此对我国农历 2 4个节气进行了新的量化解释。云方法已经用于数据开采、智能控制、跳频电台和大系统效能评估中 ,取得明显的效果。

Knowledge representation in AI has been a bottleneck for years. And the difficulty is uncertainty hidden in qualitative concepts, that is the randomness and fuzziness. At this junction, this paper presents a new concept of cloud models with three digital characteristics: expected value Ex, entropy En, and hyper entropy He. This methodology has effectively made mapping between quantitative and qualitative knowledge much easier at any time. A cloud drop, that is a quantitative value, representing the qualitative concept can be measured by contributions. A new explanation for the 24 solar terms in lunar calendar is given as well. The cloud models have been used in data mining, intelligent control, hopping frequency technique,system evaluation,and so on. [