Intensional FOL for reasoning about probabilities and probabilistic logic programming
Intensional FOL for reasoning about probabilities and probabilistic logic programming
复制标题
用于概率推理和概率逻辑编程的内涵 FOL
DOI：
10.1504/ijiids.2018.10012745
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. Prasad
中科院分区：
文献类型：
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作者：
Z. Majkic;B. Prasad
对于概率的计算和概率的推理来说，具有定义良好的语法和语义的逻辑是很重要的。目前基于尼尔森概率结构/逻辑和线性不等式的概率推理方法存在一定的不足。在本研究中，我们对这些方法进行了完整的修订，并表明概率推理逻辑可以自然地嵌入到具有内涵抽象的2值内涵一阶逻辑（FOL）中，通过避免当前由两个不同的2值逻辑组成的临时系统：一个用于较低层次的经典命题逻辑，另一个用于较高层次的具有概率变量的概率约束的新2值逻辑。所得的理论结果可应用于概率逻辑规划。
It is important to have a logic, both for computation of probabilities and for reasoning about probabilities, with well-defined syntax and semantics. The current approaches, which are based on Nilsson's probability structures/logics as well as linear inequalities, to reason about probabilities, have some deficiencies. In this research, we have presented a complete revision of those approaches and have shown that the logic for reasoning about probabilities can be naturally embedded into a 2-valued intensional first-order logic (FOL) with intensional abstraction, by avoiding current ad-hoc system composed of two different 2-valued logics: one for the classical propositional logic at a lower-level and a new one, at a higher-level, for probabilistic constraints with probabilistic variables. The theoretical results that are obtained are applied to probabilistic logic programming.