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We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a…

Many classical planning frameworks are built on first-order languages. The first-order expressive power is desirable for compactly representing actions via schemas, and for specifying quantified conditions such as $\neg\exists…

Logic in Computer Science · Computer Science 2020-06-04 Andrés Occhipinti Liberman , Andreas Achen , Rasmus Kræmmer Rendsvig

Formalizing syntactic proofs of properties of logics, programming languages, security protocols, and other formal systems is a significant challenge, in large part because of the obligation to handle name-binding correctly. We present an…

Logic in Computer Science · Computer Science 2007-05-23 James Cheney

Ten years ago, it was shown that nominal techniques can be used to design coalgebraic data types with variable binding, so that alpha-equivalence classes of infinitary terms are directly endowed with a corecursion principle. We introduce…

Logic in Computer Science · Computer Science 2025-11-05 Rémy Cerda

We develop a second-order extension of intuitionistic modal logic, allowing quantification over propositions, both syntactically and semantically. A key feature of second-order logic is its capacity to define positive connectives from the…

Logic in Computer Science · Computer Science 2026-02-09 Justus Becker , Anupam Das , Sonia Marin , Paaras Padhiar

We present a logic named L_{LF} whose intended use is to formalize properties of specifications developed in the dependently typed lambda calculus LF. The logic is parameterized by the LF signature that constitutes the specification. Atomic…

Logic in Computer Science · Computer Science 2022-04-12 Gopalan Nadathur , Mary Southern

Probabilistic neurosymbolic learning seeks to integrate neural networks with symbolic programming. Many state-of-the-art systems rely on a reduction to the Probabilistic Weighted Model Counting Problem (PWMC), which requires computing a…

Artificial Intelligence · Computer Science 2025-01-31 Thomas Jean-Michel Valentin , Luisa Sophie Werner , Pierre Genevès , Nabil Layaïda

Nominal algebra includes $\alpha$-equality and freshness constraints on nominal terms endowed with a nominal set semantics that facilitates reasoning about languages with binders. Nominal unification is decidable and unitary, however, its…

Logic in Computer Science · Computer Science 2024-12-18 Ali K. Caires-Santos , Maribel Fernández , Daniele Nantes-Sobrinho

We define the syntax and reduction relation of a recursively typed lambda calculus with a parallel case-function (a parallel conditional). The reduction is shown to be confluent. We interpret the recursive types as information systems in a…

Logic in Computer Science · Computer Science 2008-06-12 Fritz Müller

We propose a hybrid-dynamic first-order logic as a formal foundation for specifying and reasoning about reconfigurable systems. As the name suggests, the formalism we develop extends (many-sorted) first-order logic with features that are…

Logic in Computer Science · Computer Science 2019-05-13 Daniel Găină , Ionuţ Ţuţu

We extend the {\lambda}-calculus with constructs suitable for relational and functional-logic programming: non-deterministic choice, fresh variable introduction, and unification of expressions. In order to be able to unify…

Programming Languages · Computer Science 2021-03-02 Pablo Barenbaum , Federico Lochbaum , Mariana Milicich

Parameterization extends higher-order processes with the capability of abstraction (akin to that in lambda-calculus), and is known to be able to enhance the expressiveness. This paper focuses on the parameterization of names, i.e. a…

Logic in Computer Science · Computer Science 2015-08-21 Xian Xu , Qiang Yin , Huan Long

Defeasible logic is a rule-based nonmonotonic logic, with both strict and defeasible rules, and a priority relation on rules. We show that inference in the propositional form of the logic can be performed in linear time. This contrasts…

Artificial Intelligence · Computer Science 2009-09-29 Michael J. Maher

Possibilistic logic is a well-known graded logic of uncertainty suitable to reason under incomplete information and partially inconsistent knowledge, which is built upon classical first order logic. There exists for Possibilistic logic a…

Artificial Intelligence · Computer Science 2013-01-31 Teresa Alsinet , Lluis Godo , Sandra Sandri

Within classical propositional logic, assigning probabilities to formulas is shown to be equivalent to assigning probabilities to valuations. A novel notion of probabilistic entailment enjoying desirable properties of logical consequence is…

Logic · Mathematics 2016-01-13 Joao Rasga , Cristina Sernadas , Amilcar Sernadas

Possibility theory offers a framework where both Lehmann's "preferential inference" and the more productive (but less cautious) "rational closure inference" can be represented. However, there are situations where the second inference does…

Artificial Intelligence · Computer Science 2013-02-18 Salem Benferhat , Didier Dubois , Henri Prade

This paper is concerned with the form of typed name binding used by the FreshML family of languages. Its characteristic feature is that a name binding is represented by an abstract (name,value)-pair that may only be deconstructed via the…

Programming Languages · Computer Science 2015-07-01 Andrew M. Pitts , Mark R. Shinwell

In many instances in first order logic or computable algebra, classical theorems show that many problems are undecidable for general structures, but become decidable if some rigidity is imposed on the structure. For example, the set of…

Discrete Mathematics · Computer Science 2017-08-08 Emmanuel Jeandel

In this paper, we show that the derivability problem for the primal propositional logic remains solvable in polynomial time upon adding a certain form of the principle of equivalent form substitution; and that, upon adding another form of…

Logic · Mathematics 2020-12-01 Inga Lev

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g.,…

Machine Learning · Computer Science 2020-08-11 Zhiting Hu , Xuezhe Ma , Zhengzhong Liu , Eduard Hovy , Eric Xing
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