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Modular reasoning about class invariants is challenging in the presence of dependencies among collaborating objects that need to maintain global consistency. This paper presents semantic collaboration: a novel methodology to specify and…

Software Engineering · Computer Science 2014-05-08 Nadia Polikarpova , Julian Tschannen , Carlo A. Furia , Bertrand Meyer

We develop techniques at the interface between differential algebra and model theory to study the following problems of exponential algebraicity: Does a given algebraic differential equation admits an exponentially algebraic solution, that…

Logic · Mathematics 2025-10-31 Rémi Jaoui , Jonathan Kirby

We propose a decision-theoretic framework for computational complexity, complementary to classical theory: moving from syntactic exactness (Turing / Shannon) to semantic simulability (Le Cam). While classical theory classifies problems by…

Statistics Theory · Mathematics 2026-01-01 Deniz Akdemir

In this paper we propose a new approach to realizability interpretations for nonstandard arithmetic. We deal with nonstandard analysis in the context of (semi)intuitionistic realizability, focusing on the Lightstone-Robinson construction of…

Logic in Computer Science · Computer Science 2024-02-14 Bruno Dinis , Étienne Miquey

For over a decade, the hypercomputation movement has produced computational models that in theory solve the algorithmically unsolvable, but they are not physically realizable according to currently accepted physical theories. While…

Logic · Mathematics 2014-08-12 Aran Nayebi

In this article, we consider a simple representation for real numbers and propose top-down procedures to approximate various algebraic and transcendental operations with arbitrary precision. Detailed algorithms and proofs are provided to…

Numerical Analysis · Computer Science 2015-09-22 Sarmen Keshishzadeh , Jan Friso Groote

The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for communicating agents using Hebbian learning and working on…

Neurons and Cognition · Quantitative Biology 2007-05-23 Andras Lorincz

Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users. The learner faces a number of significant constraints: learning should be online, incremental and few-shot, as it…

Computation and Language · Computer Science 2023-05-08 Jonghyuk Park , Alex Lascarides , Subramanian Ramamoorthy

Due to recent empirical successes, the options framework for hierarchical reinforcement learning is gaining increasing popularity. Rather than learning from rewards which suffers from the curse of dimensionality, we consider learning an…

Machine Learning · Computer Science 2021-02-16 Zhiyu Zhang , Ioannis Paschalidis

We show that every finite semilattice can be represented as an atomized semilattice, an algebraic structure with additional elements (atoms) that extend the semilattice's partial order. Each atom maps to one subdirectly irreducible…

Rings and Algebras · Mathematics 2021-02-17 Fernando Martin-Maroto , Gonzalo G. de Polavieja

The point of this work is to explore axiomatisations of concurrent computation using the technology of proof theory and realizability. To deal with this problem, we redefine the Concurrent Realizability of Beffara using as realizers a…

Logic in Computer Science · Computer Science 2021-12-30 Emmanuel Beffara , Félix Castro , Mauricio Guillermo

Skolemization, with Herbrand's theorem, underpins automated theorem proving and various transformations in computer science and mathematics. Skolemization removes strong quantifiers by introducing new function symbols, enabling efficient…

Logic in Computer Science · Computer Science 2025-01-28 Matthias Baaz , Mariami Gamsakhurdia , Rosalie Iemhoff , Raheleh Jalali

The focus of these lecture notes is on abstract models and basic ideas and results that relate to the operational semantics of programming languages largely conceived. The approach is to start with an abstract description of the computation…

Programming Languages · Computer Science 2025-10-15 Roberto M. Amadio

We present a new approach to automated reasoning about higher-order programs by extending symbolic execution to use behavioral contracts as symbolic values, enabling symbolic approximation of higher-order behavior. Our approach is based on…

Programming Languages · Computer Science 2012-04-27 Sam Tobin-Hochstadt , David Van Horn

We show that it is possible to define a realizability interpretation for the $\Sigma_2$-fragment of classical Analysis using G\"odel's System T only. This supplements a previous result of Schwichtenberg regarding bar recursion at types 0…

Logic · Mathematics 2015-01-30 Danko Ilik

Computability logic (CL) is a systematic formal theory of computational tasks and resources, which, in a sense, can be seen as a semantics-based alternative to (the syntactically introduced) linear logic. With its expressive and flexible…

Logic in Computer Science · Computer Science 2010-03-26 Giorgi Japaridze

We axiomatize the provability logic of $\HA$ and prove its decidability. Furthermore, we axiomatize the preservativity and relative admissibility relations for several modal logics extending iK4. A principal technical tool is the…

Logic · Mathematics 2026-01-05 Mojtaba Mojtahedi

The technique of "classical realizability" is an extension of the method of "forcing"; it permits to extend the Curry-Howard correspondence between proofs and programs, to Zermelo-Fraenkel set theory and to build new models of ZF, called…

Logic in Computer Science · Computer Science 2018-03-20 Jean-Louis Krivine

The classical simulation of physical processes using standard models of computation is fraught with problems. On the other hand, attempts at modelling real-world computation with the aim of isolating its hypercomputational content have…

Logic · Mathematics 2009-04-21 S. Barry Cooper

With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.…

Machine Learning · Computer Science 2020-06-24 Murray Shanahan , Kyriacos Nikiforou , Antonia Creswell , Christos Kaplanis , David Barrett , Marta Garnelo
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