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A recent strand of research in structural proof theory aims at exploring the notion of analytic calculi (i.e. those calculi that support general and modular proof-strategies for cut elimination), and at identifying classes of logics that…

Logic in Computer Science · Computer Science 2021-02-24 Jinsheng Chen , Giuseppe Greco , Alessandra Palmigiano , Apostolos Tzimoulis

We show that it is coNP-complete to decide whether a given proof structure of pomset logic is a correct proof net, using the graph-theoretic used in a previous paper of ours (arXiv:1901.10247).

Logic in Computer Science · Computer Science 2023-01-24 Lê Thành Dũng Nguyên

Lawvere showed that generalised metric spaces are categories enriched over $[0, \infty]$, the quantale of the positive extended reals. The statement of enrichment is a quantitative analogue of being a preorder. Towards seeking a logic for…

Logic in Computer Science · Computer Science 2024-02-14 Giorgio Bacci , Radu Mardare , Prakash Panangaden , Gordon Plotkin

This paper presents simple, syntactic strong normalization proofs for the simply-typed lambda-calculus and the polymorphic lambda-calculus (system F) with the full set of logical connectives, and all the permutative reductions. The…

Logic in Computer Science · Computer Science 2008-04-17 Aleksander Wojdyga

Agentic theorem provers often introduce intermediate lemmas, proof sketches, or subgoal decompositions before returning to tactic-level search. This can look like an expensive detour: if proving lemmas is itself hard, why should a learned…

Machine Learning · Computer Science 2026-05-11 Sho Sonoda , Shunta Akiyama , Yuya Uezato

Artificial intelligence assisted mathematical proof has become a highly focused area nowadays. One key problem in this field is to generate formal mathematical proofs from natural language proofs. Due to historical reasons, the formal proof…

Programming Languages · Computer Science 2024-05-14 Lihan Xie , Zhicheng Hui , Qinxiang Cao

Mathematical psychology has a long tradition of modeling probabilistic choice via distribution-free random utility models and associated random preference models. For such models, the predicted choice probabilities often form a bounded and…

Optimization and Control · Mathematics 2017-10-10 Clintin P. Davis-Stober , Jean-Paul Doignon , Samuel Fiorini , François Glineur , Michel Regenwetter

With help of a compact Prolog-based theorem prover for Intuitionistic Propositional Logic, we synthesize minimal assumptions under which a given formula formula becomes a theorem. After applying our synthesis algorithm to cover basic…

Logic in Computer Science · Computer Science 2022-05-13 Paul Tarau

We establish new, and surprisingly tight, connections between propositional proof complexity and finite model theory. Specifically, we show that the power of several propositional proof systems, such as Horn resolution, bounded-width…

Logic in Computer Science · Computer Science 2023-06-22 Erich Grädel , Martin Grohe , Benedikt Pago , Wied Pakusa

This article precisely defines huge proofs within the system of Natural Deduction for the Minimal implicational propositional logic \mil. This is what we call an unlimited family of super-polynomial proofs. We consider huge families of…

Logic in Computer Science · Computer Science 2021-03-25 Edward Hermann Haeusler

Conjecturing and theorem proving are activities at the center of mathematical practice and are difficult to separate. In this paper, we propose a framework for completing incomplete conjectures and incomplete proofs. The framework can turn…

Artificial Intelligence · Computer Science 2024-01-25 Salwa Tabet Gonzalez , Predrag Janičić , Julien Narboux

This paper presents a formal theory which describes propositional binary logic as a semantically closed formal language, and allows for syntactically and semantically well-formed formulae, formal proofs (demonstrability in Hilbertian…

Logic in Computer Science · Computer Science 2011-06-21 Nicolaie Popescu-Bodorin , Luminita State

Probabilistic logic reasoning is a central component of such cognitive architectures as OpenCog. However, as an integrative architecture, OpenCog facilitates cognitive synergy via hybridization of different inference methods. In this paper,…

Artificial Intelligence · Computer Science 2019-07-11 Alexey Potapov , Anatoly Belikov , Vitaly Bogdanov , Alexander Scherbatiy

While probability theory is normally applied to external environments, there has been some recent interest in probabilistic modeling of the outputs of computations that are too expensive to run. Since mathematical logic is a powerful tool…

Artificial Intelligence · Computer Science 2016-10-10 Scott Garrabrant , Benya Fallenstein , Abram Demski , Nate Soares

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional…

Artificial Intelligence · Computer Science 2007-05-23 Kristian Kersting , Luc De Raedt

Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks. However, it remains an open…

Computation and Language · Computer Science 2014-10-16 Samuel R. Bowman , Christopher Potts , Christopher D. Manning

Within the framework of computable infinitary continuous logic, we develop a system of hyperarithmetic numerals. These numerals are infinitary sentences in a metric language $L$ that have the same truth value in every interpretation of $L$.…

Logic · Mathematics 2022-11-03 Caleb M. H. Camrud , Timothy H. McNicholl

Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable…

Artificial Intelligence · Computer Science 2012-02-20 Vibhav Gogate , Pedro Domingos

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…

Artificial Intelligence · Computer Science 2025-09-15 Dmitry Lesnik , Tobias Schäfer

Neural symbolic processing aims to combine the generalization of logical learning approaches and the performance of neural networks. The Neural Theorem Proving (NTP) model by Rocktaschel et al (2017) learns embeddings for concepts and…

Machine Learning · Computer Science 2019-06-18 Michiel de Jong , Fei Sha