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PyReason: Software for Open World Temporal Logic

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arxiv 2302.13482 v3 pith:YZUGV6K4 submitted 2023-02-27 cs.LO cs.AIcs.PL

classification cs.LOcs.AIcs.PL
keywords pyreasonlogicimplementationinferencereasoningvariousannotateddifferentiable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets

    cs.AI 2025-08 conditional novelty 6.0 of 10

    A probabilistic-circuit model over rule subsets lets knowledge graph completion use 70-96% fewer rules while retaining about 91% of full-rule-set accuracy.

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