arXiv:2509.10326cs.AI
State Algebra for Propositional Logic
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Abstract
This paper presents State Algebra, a novel framework designed to represent and manipulate propositional logic using algebraic methods. The framework is structured as a hierarchy of three representations: Set, Coordinate, and Row Decomposition. These representations anchor the system in well-known semantics while facilitating the computation using a powerful algebraic engine. A key aspect of State Algebra is its flexibility in representation. We show that although the default reduction of a state vector is not canonical, a unique canonical form can be obtained by applying a fixed variable order during the reduction process. This highlights a trade-off: by foregoing guaranteed canonicity, the framework gains increased flexibility, potentially leading to more compact representations of certain classes of problems. We explore how this framework provides tools to articulate both search-based and knowledge compilation algorithms and discuss its natural extension to probabilistic logic and Weighted Model Counting.
Hi HN, I'm one of the creators. I've been tackling the problem of managing complex business rules for years (especially in fintech), and wanted a lightweight, Python-native tool that was built on a solid theoretical foundation. We built vector-logic (it's on PyPI) and wrote a…
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