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形式可微（即模糊）一阶逻辑的采用。本文提出PyReason——一个基于广义带注释逻辑的软件框架，该框架既能涵盖当前主流的可微逻辑体系，又能通过时序扩展支持有限时间区间内的推理，并具备开放世界推理能力。此外，PyReason被设计为直接支持图结构（如知识图谱、社交网络、生物网络等）上的推理，可生成完全可解释的推理轨迹，并包含类型检查与高效内存实现等实用特性。本文综述了我们集成到该实现中的广义带注释逻辑的多种扩展方案，介绍了基于现代高效Python实现的可扩展精确演绎推理方法，并给出了一组实验验证。PyReason代码开源地址：github.com/lab-v2/pyreason。