Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Vienna University of Technology

Abstract

Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.

Details

Original languageEnglish
Title of host publicationProceedings of the 33rd AAAI Conference on Artificial Intelligence
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventAAAI Conference on Artificial Intelligence - Honolulu, United States
Duration: 27 Jan 20191 Feb 2019
Conference number: 33
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence
Abbreviated titleAAAI
CountryUnited States
CityHonolulu
Period27/01/201901/02/2019
Internet address

    Research areas

  • Knowledge Representation, Nonmonotonic Reasoning, Answer-Set Programming, Lazy-Grounding, Normalization

ID: 29616742