๐ฎ
๐ฎ
The Ethereal
Foundations for an Abstract Proof Theory in the Context of Horn Rules
April 12, 2023 ยท The Ethereal ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tim S. Lyon, Piotr Ostropolski-Nalewaja
arXiv ID
2304.05697
Category
cs.LO: Logic in CS
Cross-listed
cs.DM,
cs.DS,
math.LO
Citations
3
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We introduce a novel, logic-independent framework for the study of sequent-style proof systems, which covers a number of proof-theoretic formalisms and concrete proof systems that appear in the literature. In particular, we introduce a generalized form of sequents, dubbed 'g-sequents,' which are taken to be binary graphs of typical, Gentzen-style sequents. We then define a variety of 'inference rule types' as sets of operations that act over such objects, and define 'abstract (sequent) calculi' as pairs consisting of a set of g-sequents together with a finite set of operations. Our approach permits an analysis of how certain inference rule types interact in a general setting, demonstrating under what conditions rules of a specific type can be permuted with or simulated by others, and being applicable to any sequent-style proof system that fits within our framework. We then leverage our permutation and simulation results to establish generic calculus and proof transformation algorithms, which show that every abstract calculus can be effectively transformed into a lattice of polynomially equivalent abstract calculi. We determine the complexity of computing this lattice and compute the relative sizes of proofs and sequents within distinct calculi of a lattice. We recognize that top and bottom elements in lattices correspond to many known deep-inference nested sequent systems and labeled sequent systems (respectively) for logics characterized by Horn properties.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Logic in CS
๐ฎ
๐ฎ
The Ethereal
Safe Reinforcement Learning via Shielding
๐ฎ
๐ฎ
The Ethereal
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
๐ฎ
๐ฎ
The Ethereal
Heterogeneous substitution systems revisited
๐ฎ
๐ฎ
The Ethereal
Omega-Regular Objectives in Model-Free Reinforcement Learning
๐ฎ
๐ฎ
The Ethereal