PyReason: Software for Open World Temporal Logic
This work provides a practical tool for researchers and practitioners in neuro-symbolic reasoning, though it is incremental as it builds on existing generalized annotated logic.
The authors tackled the need for a software framework supporting open world temporal logic by introducing PyReason, which captures differentiable logics and temporal extensions for inference over finite periods, resulting in a scalable Python-based implementation with features like explainable traces and memory efficiency.
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.