Search Results for author: Yuliya Lierler

Found 19 papers, 1 papers with code

Elementary Sets for Logic Programs

no code implementations15 Jul 2023 Martin Gebser, Joohyung Lee, Yuliya Lierler

We propose the notion of an elementary set, which is almost equivalent to the notion of an elementary loop for nondisjunctive programs, but is simpler, and, unlike elementary loops, can be extended to disjunctive programs without producing unintuitive results.

System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics

no code implementations29 Mar 2023 Daniel Bresnahan, Nicholas Hippen, Yuliya Lierler

We evaluate the impact of Predictor when used as a guide for rewritings produced by the answer set programming rewriting tools Projector and Lpopt.

Unifying Framework for Optimizations in non-boolean Formalisms

no code implementations16 Jun 2022 Yuliya Lierler

Search-optimization problems are plentiful in scientific and engineering domains.

An Abstract View on Optimizations in Propositional Frameworks

no code implementations13 Jun 2022 Yuliya Lierler

Automated reasoning and knowledge representation are the subfields of AI that are particularly vested in these developments.

Constraint Answer Set Programming: Integrational and Translational (or SMT-based) Approaches

no code implementations17 Jul 2021 Yuliya Lierler

Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories.

Scheduling

Modular Answer Set Programming as a Formal Specification Language

no code implementations5 Aug 2020 Pedro Cabalar, Jorge Fandinno, Yuliya Lierler

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 problem encoded by P, regardless of the problem instance.

SMT-based Constraint Answer Set Solver EZSMT+

no code implementations8 May 2019 Da Shen, Yuliya Lierler

Constraint answer set programming integrates answer set programming with constraint processing.

The informal semantics of Answer Set Programming: A Tarskian perspective

no code implementations26 Jan 2019 Marc Denecker, Yuliya Lierler, Miroslaw Truszczynski, Joost Vennekens

In 1999, the seminal papers on answer set programming proposed to use this logic for a different purpose, namely, to model and solve search problems.

Strong Equivalence and Program Structure in Arguing Essential Equivalence between Logic Programs

no code implementations26 Jan 2019 Yuliya Lierler

Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing different knowledge representation formalisms.

Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter

no code implementations14 Feb 2017 Marcello Balduccini, Yuliya Lierler

One of the main contributions of this paper is the first comprehensive account of the constraint answer set language and solver EZCSP, a mainstream representative of this research area that has been used in various successful applications.

Disjunctive Answer Set Solvers via Templates

no code implementations6 Oct 2015 Remi Brochenin, Yuliya Lierler, Marco Maratea

A fundamental task in answer set programming is to compute stable models, i. e., solutions of logic programs.

Proceedings of Answer Set Programming and Other Computing Paradigms (ASPOCP 2013), 6th International Workshop, August 25, 2013, Istanbul, Turkey

no code implementations28 Dec 2013 Michael Fink, Yuliya Lierler

This volume contains the papers presented at the sixth workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2013) held on August 25th, 2013 in Istanbul, co-located with the 29th International Conference on Logic Programming (ICLP 2013).

Abstract Modular Systems and Solvers

no code implementations20 Dec 2013 Yuliya Lierler, Miroslaw Truszczynski

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience and computational benefits.

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