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In response to a concern raised by Horty, this paper develops a two-tiered, preference-based semantic framework for modeling defeasible conditional obligations. The paper extends a Hansson-Lewis style preference semantics for dyadic deontic…

Logic in Computer Science · Computer Science 2026-05-01 Xavier Parent

In order to enrich dynamic semantic theories with a `pragmatic' capacity, we combine dynamic and nonmonotonic (preferential) logics in a modal logic setting. We extend a fragment of Van Benthem and De Rijke's dynamic modal logic with…

cmp-lg · Computer Science 2008-02-03 Jan Jaspars , Megumi Kameyama

This paper outlines a general formal framework for reasoning systems, intended to support future analysis of inference architectures across domains. We model reasoning systems as structured tuples comprising phenomena, explanation space,…

Artificial Intelligence · Computer Science 2025-08-05 Saleh Nikooroo , Thomas Engel

We view the syntax-based approaches to default reasoning as a model-based diagnosis problem, where each source giving a piece of information is considered as a component. It is formalized in the ATMS framework (each source corresponds to an…

Artificial Intelligence · Computer Science 2013-02-28 Jerome Lang

Counterfactual reasoning and contextuality is defined and critically evaluated with regard to its nonempirical content. To this end, a uniqueness property of states, explosion views and link observables are introduced. If only a single…

Quantum Physics · Physics 2009-11-10 Karl Svozil

We discuss quantum non-locality and contextuality, emphasising logical and structural aspects. We also show how the same mathematical structures arise in various areas of classical computation.

Quantum Physics · Physics 2014-07-01 Samson Abramsky

Human language is full of compositional syntactic structures, and although neural networks have contributed to groundbreaking improvements in computer systems that process language, widely-used neural network architectures still exhibit…

Computation and Language · Computer Science 2023-05-19 Brian DuSell

Deep learning continues to revolutionize an ever-growing number of critical application areas including healthcare, transportation, finance, and basic sciences. Despite their increased predictive power, model transparency and human…

Machine Learning · Computer Science 2020-04-28 Benjamin Shickel , Parisa Rashidi

Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly incomplete, which acts as a significant barrier to their more…

Artificial Intelligence · Computer Science 2021-05-12 Steven Schockaert , Yazmín Ibáñez-García , Víctor Gutiérrez-Basulto

Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs…

Artificial Intelligence · Computer Science 2025-12-23 Maksim Gladyshev , Natasha Alechina , Mehdi Dastani , Dragan Doder , Brian Logan

Targeted syntactic evaluations of language models ask whether models show stable preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Most targeted syntactic evaluation datasets ask models to make these…

Computation and Language · Computer Science 2022-12-20 Koustuv Sinha , Jon Gauthier , Aaron Mueller , Kanishka Misra , Keren Fuentes , Roger Levy , Adina Williams

Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models…

Computation and Language · Computer Science 2021-04-22 Ximing Lu , Peter West , Rowan Zellers , Ronan Le Bras , Chandra Bhagavatula , Yejin Choi

Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to…

Artificial Intelligence · Computer Science 2019-05-14 Tiantian Gao

When we work with information from multiple sources, the formalism each employs to handle uncertainty may not be uniform. In order to be able to combine these knowledge bases of different formats, we need to first establish a common basis…

Artificial Intelligence · Computer Science 2013-02-18 Choh Man Teng

Modal logics are widely used in multi-agent systems to reason about actions, abilities, norms, or epistemic states. Combined with description logic languages, they are also a powerful tool to formalise modal aspects of ontology-based…

Logic in Computer Science · Computer Science 2023-07-25 Tiziano Dalmonte , Andrea Mazzullo , Ana Ozaki , Nicolas Troquard

Formalisms for specifying statistical models, such as probabilistic-programming languages, typically consist of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict the…

Databases · Computer Science 2015-01-06 Vince Barany , Balder ten Cate , Benny Kimelfeld , Dan Olteanu , Zografoula Vagena

Reasoning about dynamic systems with a fine-grained temporal and numeric resolution presents significant challenges for logic-based approaches like Answer Set Programming (ASP). To address this, we introduce and elaborate upon a novel…

Artificial Intelligence · Computer Science 2026-01-14 Pedro Cabalar , Martín Diéguez , François Olivier , Torsten Schaub , Igor Stéphan

Sequence theories are an extension of theories of strings with an infinite alphabet of letters, together with a corresponding alphabet theory (e.g. linear integer arithmetic). Sequences are natural abstractions of extendable arrays, which…

Logic in Computer Science · Computer Science 2023-08-02 Artur Jeż , Anthony W. Lin , Oliver Markgraf , Philipp Rümmer

The notion of a non-deterministic logical matrix (where connectives are interpreted as multi-functions) extends the traditional semantics for propositional logics based on logical matrices (where connectives are interpreted as functions).…

Logic · Mathematics 2024-12-19 Carlos Caleiro , Pedro Filipe , Sérgio Marcelino

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable…

Computation and Language · Computer Science 2020-02-18 Nitish Gupta , Kevin Lin , Dan Roth , Sameer Singh , Matt Gardner
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