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ASP Essentials: Modelling and Efficient Solving (Invited Paper)

Authors Giuseppe Mazzotta , Francesco Ricca

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
  • Computing methodologies → Logic programming and answer set programming
  • Computing methodologies → Artificial intelligence
Keywords
  • Answer Set Programming
  • ASP with Quantifiers
  • Grounding Bottleneck
  • Compilation-based ASP solving
  • Neurosymbolic AI
  • LLMs

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Abstract

Answer Set Programming (ASP) is a logic-based Knowledge Representation and Reasoning (KRR) paradigm that facilitates rapid prototyping of solutions for complex problems. It is particularly effective for tackling Deep Reasoning tasks involving exponentially large search spaces, such as combinatorial search and optimization. While getting started with ASP is relatively easy, mastering its advanced constructs and scaling solutions to real-world problem sizes can be challenging. This paper provides an introduction to ASP, guiding the reader from the fundamentals of the language to the application of programming methodologies and the computation of answer sets. Beyond the core framework, the paper also examines selected extensions of ASP that enable the modeling of complex problems, as well as compilation techniques designed to enhance solving efficiency. Furthermore, it mentions some recent tools that combine ASP with LLMs.

Cite As Get BibTex

Giuseppe Mazzotta and Francesco Ricca. ASP Essentials: Modelling and Efficient Solving (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.RW.2024/2025.8

Author Details

Giuseppe Mazzotta
  • University of Calabria, Rende, Italy
Francesco Ricca
  • University of Calabria, Rende, Italy

Funding

This work was supported by the Italian Ministry of Industrial Development (MISE) under project EI-TWIN n. F/310168/05/X56 CUP B29J24000680005; and by the Italian Ministry of Research (MUR) under projects: PNRR FAIR - Spoke 9 - WP 9.1 CUP H23C22000860006, and Tech4You CUP H23C22000370006.

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