中文摘要：

从动态诱发性开发了图(DCD ) 模型，为知识表示的一条新途径并且当动态不明确的诱发性图(DUCG ) 被论述，推理说出，它集中于复杂不明确的诱发性和有效概率的推理的紧缩的表示。存在紧缩的表示当模特儿，在贝叶斯的网络(BN ) 的推理在珍视单人赛的情况中是适用的，这被指出，但是不能合适在多珍视的情况中被使用。DUCG 克服这个问题并且在远处。DUCG 的主要特征是：1 ) 简洁地并且图形地代表复杂有条件的概率分布(CPD ) ，不管是否盒子珍视单人赛或多珍视；2 ) 能执行在不完全的知识表示的情况中的准确推理；3 ) 在另外的计算前简化在观察上有条件的图形的知识库，以便问题的规模和复杂性能指数地被减少；4 ) 由(a) 逻辑操作组成为给定的观察和(b) 在担心发现所有可能的假设的有效二拍子的圆舞推理算法为这些假设的概率计算；并且 5 ) 更不依靠参数精确性。一个警报系统例子被提供说明 DUCG 方法论。

英文摘要：

Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.