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We present a type theory with some proof-irrelevance built into the conversion rule. We argue that this feature is useful when type theory is used as the logical formalism underlying a theorem prover. We also show a close relation with the…

Logic in Computer Science · Computer Science 2015-07-01 Benjamin Werner

In this work we suggest the use of a set-theoretical interpretation of semantic tableaux for teaching propositional logic. If the student has previous notions of basic set theory, this approach to semantical tableaux can clarify her the way…

Computers and Society · Computer Science 2015-07-15 Nino Guallart , Angel Nepomuceno-Fernandez

We introduce a new form of logical relation which, in the spirit of metric relations, allows us to assign each pair of programs a quantity measuring their distance, rather than a boolean value standing for their being equivalent. The…

Logic in Computer Science · Computer Science 2019-04-30 Ugo Dal Lago , Francesco Gavazzo , Akira Yoshimizu

Default logic can be regarded as a mechanism to represent families of belief sets of a reasoning agent. As such, it is inherently second-order. In this paper, we study the problem of representability of a family of theories as the set of…

Logic in Computer Science · Computer Science 2007-05-23 Victor Marek , Jan Treur , Miroslaw Truszczynski

Arrangement theory plays an essential role in the study of the unfolding model used in many fields. This paper describes how arrangement theory can be usefully employed in solving the problems of counting (i) the number of admissible…

Combinatorics · Mathematics 2013-01-11 Hidehiko Kamiya , Akimichi Takemura , Norihide Tokushige

Semantic theories of natural language associate meanings with utterances by providing meanings for lexical items and rules for determining the meaning of larger units given the meanings of their parts. Meanings are often assumed to combine…

cmp-lg · Computer Science 2008-02-03 Mary Dalrymple , John Lamping , Fernando Pereira , Vijay Saraswat

This brief brochure is intended to present a philosophical theory known as relational materialism. We introduce the postulates and principles of the theory, articulating its ontological and epistemological content using the language of…

History and Philosophy of Physics · Physics 2024-09-05 Bekir Baytaş , Ozan Ekin Derin

Justification theory is a unifying semantic framework. While it has its roots in non-monotonic logics, it can be applied to various areas in computer science, especially in explainable reasoning; its most central concept is a justification:…

Artificial Intelligence · Computer Science 2020-09-23 Simon Marynissen , Bart Bogaerts , Marc Denecker

We survey aspects of prediction theory in infinitely many dimensions, with a view to the theory and applications of functional time series.

Probability · Mathematics 2021-11-24 Nicholas Hugh Bingham

This paper provides a complete suite of axioms for a version of set theory that I call Explication. Explication borrows from the two most prominent existing systems of set theory. Explication starts with class variables. After several…

Logic · Mathematics 2017-09-14 Ernest Akemann

Tarski gave a general semantics for deductive reasoning: a formula a may be deduced from a set A of formulas iff a holds in all models in which each of the elements of A holds. A more liberal semantics has been considered: a formula a may…

Artificial Intelligence · Computer Science 2007-05-23 Daniel Lehmann

Algebraic theories, sometimes called equational theories, are syntactic notions given by finitary operations and equations, such as monoids, groups, and rings. There is a well-known category-theoretic treatment of them that algebraic…

Category Theory · Mathematics 2026-03-31 Yuto Kawase

This document develops general concepts useful for extracting knowledge embedded in large graphs or datasets that have pair-wise relationships, such as cause-effect-type relations. Almost no underlying assumptions are made, other than that…

Machine Learning · Computer Science 2019-02-22 Linas Vepstas

This article aims to explain essential elements of perturbation theory and their conceptual underpinnings. It is not meant as a summary of popular perturbation methods, though some illustrative examples are given to underline the main…

History and Overview · Mathematics 2022-12-15 Nicolas Fillion , Robert M. Corless

Diversities are an extension of the concept of a metric space which assign a non-negative value to every finite set of points, rather than just pairs. A general theory of diversities has been developed which exhibits many deep analogies to…

Metric Geometry · Mathematics 2026-03-04 David Bryant , Paul Tupper

Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability distribution based on the importance of the instance in some sense. The simplicity and usefulness of the…

Machine Learning · Computer Science 2024-05-15 Masanari Kimura , Hideitsu Hino

We investigate the position that foundational theories should be modelled on ordinary computability. In this context, we investigate the metamathematics of $\Sigma$ formulas. We consider theories whose axioms are implications between…

Logic · Mathematics 2017-07-25 Andre Kornell

The work in this article is concerned with two different types of families of finite sets: separating families and splitting families (they are also called "systems"). These families have applications in combinatorial search, coding theory,…

Combinatorics · Mathematics 2019-08-16 Daniel Condon , Samuel Coskey , Luke Serafin , Cody Stockdale

Approximation theory is concerned with the ability to approximate functions by simpler and more easily calculated functions. The first question we ask in approximation theory concerns the {\it possibility of approximation}. Is the given…

Classical Analysis and ODEs · Mathematics 2007-05-23 Allan Pinkus

As complex machine learning models continue to find applications in high-stakes decision-making scenarios, it is crucial that we can explain and understand their predictions. Post-hoc explanation methods provide useful insights by…

Machine Learning · Statistics 2024-10-16 Beepul Bharti , Paul Yi , Jeremias Sulam
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