Boolean Functions with Ordered Domains in Answer Set Programming

Authors: Mario Alviano, Wolfgang Faber, Hannes Strass

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we develop a new methodology for showing when such checks can be done in deterministic polynomial time. This provides a unifying view on all currently known polynomialtime decidability results, and furthermore identifies promising new classes that go well beyond the state of the art. Our main technique consists of using an ordering on the atoms to significantly reduce the necessary number of model checks. For many standard aggregates, we show how this ordering can be automatically obtained.
Researcher Affiliation Academia Mario Alviano University of Calabria, Italy alviano@mat.unical.it Wolfgang Faber University of Huddersfield, UK wf@wfaber.com Hannes Strass Leipzig University, Germany strass@informatik.uni-leipzig.de
Pseudocode No The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical definitions, propositions, and proofs.
Open Source Code No The paper does not provide any statements about releasing open-source code for the described methodology.
Open Datasets No The paper is theoretical and does not involve experimental training on datasets. Therefore, it does not mention public datasets or their availability for training.
Dataset Splits No The paper is theoretical and does not involve empirical validation on datasets. Therefore, it does not provide dataset split information.
Hardware Specification No The paper describes theoretical work and does not report on experiments requiring specific hardware. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and focuses on logical formalisms. It does not describe an experimental setup with specific software dependencies and version numbers.
Experiment Setup No The paper describes theoretical contributions and does not present an experimental setup with hyperparameters or system-level training settings.