论文标题
概率推理，编程和概念形成的基础
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation
论文作者
论文摘要
有人认为，4值的paraciscistent真实价值（在此称为“ p-bit”）可以作为高度AI相关的概率逻辑和概率编程和概念形式的概念，数学和实用的基础。 首先表明，根据可构造双重性（CD）逻辑运行的4值P-bits的适当平均含量和重新规范化可产生PLN（概率逻辑网络）强度和信心真实值。然后，使用咖喱咖啡馆对应关系的变化将这些偏ani和概率逻辑映射到适合在基于依赖类型的编程语言中使用的概率类型。 扎克·韦伯（Zach Weber）对sorites悖论的矛盾分析被扩展为对概念边界的偏执 /概率 /概率 /模糊分析。并通过形式概念分析提出了概念形成的矛盾形式，这是基于模糊属性值学位的定义，该学位是基于对paraconsistent的概率分布的相对熵的。 这些一般要点是通过参考Opencog AGI框架中概率推理，编程和概念形成的实现来充实的，该框架以协作性多级数更新为中心。
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic programming and concept formation. First it is shown that appropriate averaging-across-situations and renormalization of 4-valued p-bits operating in accordance with Constructible Duality (CD) logic yields PLN (Probabilistic Logic Networks) strength-and-confidence truth values. Then variations on the Curry-Howard correspondence are used to map these paraconsistent and probabilistic logics into probabilistic types suitable for use within dependent type based programming languages. Zach Weber's paraconsistent analysis of the sorites paradox is extended to form a paraconsistent / probabilistic / fuzzy analysis of concept boundaries; and a paraconsistent version of concept formation via Formal Concept Analysis is presented, building on a definition of fuzzy property-value degrees in terms of relative entropy on paraconsistent probability distributions. These general points are fleshed out via reference to the realization of probabilistic reasoning and programming and concept formation in the OpenCog AGI framework which is centered on collaborative multi-algorithm updating of a common knowledge metagraph.
