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Benchmarking automated theorem proving (ATP) systems using standardized problem sets is a well-established method for measuring their performance. However, the availability of such libraries for non-classical logics is very limited. In this…

Logic in Computer Science · Computer Science 2019-04-16 Carlos Olarte , Valeria de Paiva , Elaine Pimentel , Giselle Reis

We present two deductively equivalent calculi for non-deterministic many-valued logics. One is defined by axioms and the other - by rules of inference. The two calculi are obtained from the truth tables of the logic under consideration in a…

Logic in Computer Science · Computer Science 2023-06-22 Michael Kaminski

We introduce a realisability semantics for infinitary intuitionistic set theory that is based on Ordinal Turing Machines (OTMs). We show that our notion of OTM-realisability is sound with respect to certain systems of infinitary…

Logic · Mathematics 2022-12-14 Merlin Carl , Lorenzo Galeotti , Robert Passmann

Differential linear logic (DiLL) provides a fine analysis of resource consumption in cut-elimination. We investigate the subsystem of DiLL without promotion in a deep inference formalism, where cuts are at an atomic level. In our system…

Logic in Computer Science · Computer Science 2022-01-03 Matteo Acclavio , Giulio Guerrieri

Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background knowledge. We introduce an approach that 'shrinks' the…

Artificial Intelligence · Computer Science 2026-05-18 Andrew Cropper , Filipe Gouveia , David M. Cerna

Decision transformer based sequential policies have emerged as a powerful paradigm in offline reinforcement learning (RL), yet their efficacy remains constrained by the quality of static datasets and inherent architectural limitations.…

Machine Learning · Computer Science 2026-03-05 Yihao Qin , Yuanfei Wang , Hang Zhou , Peiran Liu , Hao Dong , Yiding Ji

The successes of reinforcement learning in recent years are underpinned by the characterization of suitable reward functions. However, in settings where such rewards are non-intuitive, difficult to define, or otherwise error-prone in their…

Formal Languages and Automata Theory · Computer Science 2023-03-02 Mohammad Afzal , Sankalp Gambhir , Ashutosh Gupta , Krishna S , Ashutosh Trivedi , Alvaro Velasquez

We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior. In contrast to most of the recent work in this area, which focuses on descriptions…

Machine Learning · Computer Science 2020-02-11 Rajarshi Roy , Dana Fisman , Daniel Neider

A decision procedure for detecting valid propositional formulas is presented. It is based on the Davis-Putnam method and deals with propositional formulas that are initially converted to negational normal form. This procedure splits…

Logic in Computer Science · Computer Science 2007-05-23 Alexander Sakharov

Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, possibly accounting for the uncertainty of the environment. In this paper, we introduce the…

Artificial Intelligence · Computer Science 2025-02-19 Damiano Azzolini , Elena Bellodi , Rafael Kiesel , Fabrizio Riguzzi

In this paper, we consider the complexity of propositional proofs of classical and intuitionistic tautologies. In fact, we describe a nondeterministic polynomial-time decision procedure for intuitionistic implicational tautologies. For this…

Logic · Mathematics 2017-01-19 Grigoriy V. Bokov

Possibilistic computation tree Logic (PoCTL) is one kind of branching temporal logic combined with uncertain information in possibility theory, which was introduced in order to cope with the systematic verification on systems with uncertain…

Logic in Computer Science · Computer Science 2025-10-28 Yongming Li

This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of…

Logic in Computer Science · Computer Science 2024-11-13 Alexander V. Gheorghiu , Tao Gu , David J. Pym

Restricted non-deterministic matrices (RNmatrices) impose constraints on the rows of non-deterministic matrices (Nmatrices), filtering out "unsound" rows and retaining only "valid" ones. This yields a more expressive framework than standard…

Logic in Computer Science · Computer Science 2026-05-07 Renato R. Leme , Carlos Olarte , Elaine Pimentel

Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices…

Computation and Language · Computer Science 2026-02-25 Sanket Badhe , Deep Shah

Abstract algebraic logic is a theory that provides general tools for the algebraic study of arbitrary propositional logics. According to this theory, every logic L is associated with a matrix semantics Mod*(L). This paper is a contribution…

Logic · Mathematics 2019-08-06 T. Moraschini

The integration of reasoning, learning, and decision-making is key to build more general artificial intelligence systems. As a step in this direction, we propose a novel neural-logic architecture, called differentiable logic machine (DLM),…

Artificial Intelligence · Computer Science 2023-07-07 Matthieu Zimmer , Xuening Feng , Claire Glanois , Zhaohui Jiang , Jianyi Zhang , Paul Weng , Dong Li , Jianye Hao , Wulong Liu

We provide a new sequent calculus that enjoys syntactic cut-elimination and strongly terminating backward proof search for the intuitionistic Strong L\"ob logic $\sf{iSL}$, an intuitionistic modal logic with a provability interpretation. A…

Logic in Computer Science · Computer Science 2023-09-04 Ian Shillito , Iris van der Giessen , Rajeev Goré , Rosalie Iemhoff

How to interpret a data mining model has received much attention recently, because people may distrust a black-box predictive model if they do not understand how the model works. Hence, it will be trustworthy if a model can provide…

Machine Learning · Computer Science 2025-03-20 Zengyou He , Pengju Li , Yifan Tang , Lianyu Hu , Mudi Jiang , Yan Liu

In the past few years it has been shown that universal quantum computation can be obtained by projective measurements alone, with no need for unitary gates. This suggests that the underlying logic of quantum computing may be an algebra of…

Quantum Physics · Physics 2007-05-23 M. S. Leifer
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