The growing popularity of neuro symbolic reasoning has led to the adoption of various forms of differentiable (i.e., fuzzy) first order logic. We introduce PyReason, a software framework based on generalized annotated logic that both captures the current cohort of differentiable logics and temporal extensions to support inference over finite periods of time with capabilities for open world reasoning. Further, PyReason is implemented to directly support reasoning over graphical structures (e.g., knowledge graphs, social networks, biological networks, etc.), produces fully explainable traces of inference, and includes various practical features such as type checking and a memory-efficient implementation. This paper reviews various extensions of generalized annotated logic integrated into our implementation, our modern, efficient Python-based implementation that conducts exact yet scalable deductive inference, and a suite of experiments. PyReason is available at: github.com/lab-v2/pyreason.

翻译：神经符号推理的日益普及推动了各种可微（即模糊）一阶逻辑形式的广泛应用。本文介绍PyReason——一个基于广义注释逻辑的软件框架，该框架既能兼容当前主流可微逻辑体系，又支持时序扩展，从而可在有限时间区间内进行开放世界推理。进一步地，PyReason专为支持图结构（例如知识图谱、社交网络、生物网络等）推理而设计，能生成完全可解释的推理轨迹，并集成类型检查、内存高效实现等实用特性。本文回顾了框架中整合的广义注释逻辑扩展体系、基于现代高效Python的精确可扩展演绎推理实现方案，以及系列实验评估。PyReason开源地址：github.com/lab-v2/pyreason。