因素表示的信息空间与广义概率逻辑

Factorial information space and generalized probability logic

国内外近年来所提出的广义概率逻辑对于人工智能的发展有重要意义。能否反映变换演化的实际场景,使逻辑判断能够灵活变通,这是广义概率逻辑发展的关键。为了解决这一问题,本文的目是以信息空间作为逻辑与实际场景的接口。有了这个接口,逻辑判断就能反映变幻莫测的实际场景。本文的方法是用因素空间来定义表现论域以形成新的信息空间,将谓词中的变元取为因素,在已有的逻辑系统中加上本文所提出的背景公理,所有的推理都是在一定背景之下的推理,不同的背景会推出不同的结论。结果是新的逻辑既能维系Stone表示定理的表现要求,又能变得更加灵活有效。结论能使广义概率逻辑更有效地服务于人工智能。为了配合机制主义人工智能的需要,本文还特别提出了语法-语用对接的方法和目标驱动的逆向推理设想,最后为泛逻辑的3种连续算子对进行了数学证明。

The generalized probabilistic logic proposed in recent years is of great significance to the development of artificial intelligence.Make flexible judgment that reflects the scene of actual transformation and evolution is the key to the development of the generalized probability logic.Considering this,this paper takes the information space as the interface between logic and actual scene.With this interface,logical judgment can reflect unpredictable real situations.The method in this paper is to use factors space to define the representation domain to form the information space.Then predicate variables are taken as factors,and background axioms are added into the existing logic system.Reasoning is taken under a certain background,different backgrounds will derive different conclusions.The result is that the new logic can not only maintain the rational requirement of the Stone representation theorem but can also make decisions more flexibly and effectively.The conclusion is that the generalized probabilistic logic can serve artificial intelligence more effectively.To meet the need of mechanistic artificial intelligence,this paper proposes the grammar-pragmatic docking method and the goal-driven backward reasoning.Finally,a mathematical proof is given for three couples of continuous operators in universal logic.