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:神经符号推理越来越受欢迎,导致采用了各种形式的不同(即模糊)第一顺序逻辑。我们引入了PyReason,这是一个基于通用附加说明逻辑的软件框架,它既捕捉了目前一组不同逻辑和时间延伸,又捕捉了当前一组不同逻辑和时间延伸,以支持在有限时间内的推理,并具有开放世界推理的能力。此外,PyReason还直接支持对图形结构(例如知识图、社交网络、生物网络等)的推理,产生完全可以解释的推理痕迹,并包括诸如类型检查和记忆高效实施等各种实际特征。本文回顾了纳入我们实施的通用附加说明逻辑的各种扩展,我们现代高效的Python基础实施,该实施进行了精确且可扩缩的推理,以及一系列实验。PyReson可以在 Githhub.com/lab-v2/pyrical查阅。</s>