67 research outputs found
The Challenge of Unifying Semantic and Syntactic Inference Restrictions
While syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable
Extensional Higher-Order Paramodulation in Leo-III
Leo-III is an automated theorem prover for extensional type theory with
Henkin semantics and choice. Reasoning with primitive equality is enabled by
adapting paramodulation-based proof search to higher-order logic. The prover
may cooperate with multiple external specialist reasoning systems such as
first-order provers and SMT solvers. Leo-III is compatible with the TPTP/TSTP
framework for input formats, reporting results and proofs, and standardized
communication between reasoning systems, enabling e.g. proof reconstruction
from within proof assistants such as Isabelle/HOL. Leo-III supports reasoning
in polymorphic first-order and higher-order logic, in all normal quantified
modal logics, as well as in different deontic logics. Its development had
initiated the ongoing extension of the TPTP infrastructure to reasoning within
non-classical logics.Comment: 34 pages, 7 Figures, 1 Table; submitted articl
The Challenge of Unifying Semantic and Syntactic Inference Restrictions
International audienceWhile syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable
Graph Sequence Learning for Premise Selection
Premise selection is crucial for large theory reasoning as the sheer size of
the problems quickly leads to resource starvation. This paper proposes a
premise selection approach inspired by the domain of image captioning, where
language models automatically generate a suitable caption for a given image.
Likewise, we attempt to generate the sequence of axioms required to construct
the proof of a given problem. This is achieved by combining a pre-trained graph
neural network with a language model. We evaluated different configurations of
our method and experience a 17.7% improvement gain over the baseline.Comment: 17 page
Even shorter proofs without new variables
Proof formats for SAT solvers have diversified over the last decade, enabling
new features such as extended resolution-like capabilities, very general
extension-free rules, inclusion of proof hints, and pseudo-boolean reasoning.
Interference-based methods have been proven effective, and some theoretical
work has been undertaken to better explain their limits and semantics. In this
work, we combine the subsumption redundancy notion from (Buss, Thapen 2019) and
the overwrite logic framework from (Rebola-Pardo, Suda 2018). Natural
generalizations then become apparent, enabling even shorter proofs of the
pigeonhole principle (compared to those from (Heule, Kiesl, Biere 2017)) and
smaller unsatisfiable core generation.Comment: 21 page
Proceedings of the Deduktionstreffen 2019
The annual meeting Deduktionstreffen is the prime activity of the Special Interest Group on Deduction Systems (FG DedSys) of the AI Section of the German Society for Informatics (GI-FBKI). It is a meeting with a familiar, friendly atmosphere, where everyone interested in deduction can report on their work in an informal setting
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