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In many expert and everyday reasoning contexts it is very useful to reason on the basis of defeasible assumptions. For instance, if the information at hand is incomplete we often use plausible assumptions, or if the information is…

Logic in Computer Science · Computer Science 2018-04-25 AnneMarie Borg

This paper is about equality of proofs in which a binary predicate formalizing properties of equality occurs, besides conjunction and the constant true proposition. The properties of equality in question are those of a preordering relation,…

Logic · Mathematics 2016-04-19 K. Dosen , Z. Petric

We revisit the notion of intuitionistic equivalence and formal proof representations by adopting the view of formulas as exponential polynomials. After observing that most of the invertible proof rules of intuitionistic (minimal)…

Logic · Mathematics 2019-05-21 Taus Brock-Nannestad , Danko Ilik

We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference…

Machine Learning · Statistics 2020-09-14 Owen Thomas , Ritabrata Dutta , Jukka Corander , Samuel Kaski , Michael U. Gutmann

Inference systems are a widespread framework used to define possibly recursive predicates by means of inference rules. They allow both inductive and coinductive interpretations that are fairly well-studied. In this paper, we consider a…

Logic in Computer Science · Computer Science 2023-06-22 Francesco Dagnino

This paper introduces a new family of cognitive modal logics designed to formalize conjectural reasoning: modal systems in which cognitive contexts extend known facts with hypothetical assumptions in order to explore their consequences.…

Logic in Computer Science · Computer Science 2026-03-24 Fabio Vitali

Much of the controversy about methods for automated decision making has focused on specific calculi for combining beliefs or propagating uncertainty. We broaden the debate by (1) exploring the constellation of secondary tasks surrounding…

Artificial Intelligence · Computer Science 2013-04-11 Michael P. Wellman , David Heckerman

Relational data in its most basic form is a static collection of known facts. However, by learning to infer and deduct additional information and structure, we can massively increase the usefulness of the underlying data. One common form of…

Machine Learning · Computer Science 2019-07-30 Xavier Holt

We present a computable algorithm that assigns probabilities to every logical statement in a given formal language, and refines those probabilities over time. For instance, if the language is Peano arithmetic, it assigns probabilities to…

Artificial Intelligence · Computer Science 2020-12-09 Scott Garrabrant , Tsvi Benson-Tilsen , Andrew Critch , Nate Soares , Jessica Taylor

Evidential reasoning is cast as the problem of simplifying the evidence-hypothesis relation and constructing combination formulas that possess certain testable properties. Important classes of evidence as identifiers, annihilators, and…

Artificial Intelligence · Computer Science 2013-04-11 Yizong Cheng , Rangasami L. Kashyap

An age-old controversy in mathematics concerns the necessity and the possibility of constructive proofs. The controversy has been rekindled by recent advances which demonstrate the feasibility of a fully constructive mathematics. This…

History and Overview · Mathematics 2024-04-10 Mark Mandelkern

The reasoning with qualitative uncertainty measures involves comparative statements about events in terms of their likeliness without necessarily assigning an exact numerical value to these events. The paper is divided into two parts. In…

Logic · Mathematics 2024-03-18 Marta Bilkova , Sabine Frittella , Daniil Kozhemiachenko , Ondrej Majer

Deep inference is a proof theoretic methodology that generalizes the standard notion of inference of the sequent calculus, whereby inference rules become applicable at any depth inside logical expressions. Deep inference provides more…

Logic in Computer Science · Computer Science 2015-07-01 Ozan Kahramanogullari

Results of measurements give legitimacy to a physical theory. What if acquiring these results in the first place necessitates what the same theory considers to be an interaction? In this note, we assume that theories account for…

Quantum Physics · Physics 2019-05-01 Arne Hansen , Stefan Wolf

The vast corpus of physics equations forms an implicit network of mathematical relationships that traditional analysis cannot fully explore. This work introduces a graph-based framework combining neural networks with symbolic analysis to…

Machine Learning · Computer Science 2025-08-15 Massimiliano Romiti

Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems.…

Quantum Physics · Physics 2011-11-09 O. E. Barndorff-Nielsen , R. D. Gill , P. E. Jupp

Inspired by empirical work in neuroscience for Bayesian approaches to brain function, we give a unified probabilistic account of various types of symbolic reasoning from data. We characterise them in terms of formal logic using the…

Artificial Intelligence · Computer Science 2026-02-24 Hiroyuki Kido

Adjoint logic is a general approach to combining multiple logics with different structural properties, including linear, affine, strict, and (ordinary) intuitionistic logics, where each proposition has an intrinsic mode of truth. It has…

Logic in Computer Science · Computer Science 2024-02-05 Junyoung Jang , Sophia Roshal , Frank Pfenning , Brigitte Pientka

An analysis using classical stochastic processes is used to construct a consistent system of quantum counterfactual reasoning. When applied to a counterfactual version of Hardy's paradox, it shows that the probabilistic character of quantum…

Quantum Physics · Physics 2009-10-31 Robert B. Griffiths

We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators…

Quantum Physics · Physics 2012-08-23 Bob Coecke , Robert W. Spekkens