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Answer set programming is one of the most praised frameworks for declarative programming in general and non-monotonic reasoning in particular. There has been many efforts to extend stable model semantics so that answer set programs can use…

Logic in Computer Science · Computer Science 2012-12-04 Shahab Tasharrofi

Recently there has been an increasing interest in frameworks extending Dung's abstract Argumentation Framework (AF). Popular extensions include bipolar AFs and AFs with recursive attacks and necessary supports. Although the relationships…

Artificial Intelligence · Computer Science 2020-08-07 Gianvincenzo Alfano , Sergio Greco , Francesco Parisi , Irina Trubitsyna

Logic programming with fixed-point definitions is a useful extension of traditional logic programming. Fixed-point definitions can capture simple model checking problems and closed-world assumptions. Its operational semantics is typically…

Logic in Computer Science · Computer Science 2015-08-06 Keehang Kwon

Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much…

Artificial Intelligence · Computer Science 2024-11-11 Emanuele De Angelis , Maurizio Proietti , Francesca Toni

In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP encoding had been previously suggested, several practical…

The rise of large language models (LLMs) has sparked interest in coding assistants. While general-purpose programming languages are well supported, generating code for domain-specific languages remains a challenging problem for LLMs. In…

Artificial Intelligence · Computer Science 2025-12-22 Timo Pierre Schrader , Lukas Lange , Tobias Kaminski , Simon Razniewski , Annemarie Friedrich

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in…

Artificial Intelligence · Computer Science 2021-09-20 Akihiro Takemura , Katsumi Inoue

Propositional formulas that are equivalent in intuitionistic logic, or in its extension known as the logic of here-and-there, have the same stable models. We extend this theorem to propositional formulas with infinitely long conjunctions…

Logic in Computer Science · Computer Science 2020-02-19 Amelia Harrison , Vladimir Lifschitz , Miroslaw Truszczynski

This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second…

Artificial Intelligence · Computer Science 2020-02-19 Daniela Inclezan

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style…

Artificial Intelligence · Computer Science 2009-09-25 K. Yip , F. Zhao

We show that propositional logic and its extensions can support answer-set programming in the same way stable logic programming and disjunctive logic programming do. To this end, we introduce a logic based on the logic of propositional…

Artificial Intelligence · Computer Science 2007-05-23 Deborah East , Miroslaw Truszczynski

Probability answer set programming is a declarative programming that has been shown effective for representing and reasoning about a variety of probability reasoning tasks. However, the lack of probability aggregates, e.g. {\em expected…

Artificial Intelligence · Computer Science 2013-04-08 Emad Saad

Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs. HEX-programs extend ASP with external atoms for accessing arbitrary external information, which can introduce values that do not…

Artificial Intelligence · Computer Science 2018-06-04 Christoph Redl

In this paper, we study the problem of formal verification for Answer Set Programming (ASP), namely, obtaining a formal proof showing that the answer sets of a given (non-ground) logic program P correctly correspond to the solutions to the…

Logic in Computer Science · Computer Science 2020-09-23 Pedro Cabalar , Jorge Fandinno , Yuliya Lierler

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience and computational benefits. We introduce concepts of abstract modules and abstract modular systems to study…

Artificial Intelligence · Computer Science 2013-12-24 Yuliya Lierler , Miroslaw Truszczynski

We introduce a framework for supporting learning to program in the paradigm of Answer Set Programming (ASP), which is a declarative logic programming formalism. Based on the idea of teaching by asking the student to complete small example…

Artificial Intelligence · Computer Science 2016-08-12 Gokhan Avci , Mustafa Mehuljic , Peter Schüller

Answer Set Programming (ASP) is a declarative programming paradigm. The intrinsic complexity of the evaluation of ASP programs makes the development of more effective and faster systems a challenging research topic. This paper reports on…

Artificial Intelligence · Computer Science 2014-04-29 Mario Alviano , Carmine Dodaro , Francesco Ricca

Logic programming under the answer-set semantics nowadays deals with numerous different notions of program equivalence. This is due to the fact that equivalence for substitution (known as strong equivalence) and ordinary equivalence are…

Artificial Intelligence · Computer Science 2007-12-07 Stefan Woltran

Disjunctive Logic Programming (DLP) is a very expressive formalism: it allows for expressing every property of finite structures that is decidable in the complexity class SigmaP2 (= NP^NP). Despite this high expressiveness, there are some…

Artificial Intelligence · Computer Science 2008-02-22 Wolfgang Faber , Gerald Pfeifer , Nicola Leone , Tina Dell'Armi , Giuseppe Ielpa

Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems. While…

Artificial Intelligence · Computer Science 2026-05-01 Adam Ishay , Joohyung Lee