KR2026Proceedings of the 23rd International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning

Lisbon, Portugal. July 20-23, 2026.

Edited by
  1. Renata Wassermann
  2. Marie-Laure Mugnier
  3. Franz Baader

ISSN: 2334-1033
ISBN: 978-1-956792-18-8

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Published by

Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization

Probabilistic Reasoning within Answer Set Programming with Quantifiers

  1. Damiano Azzolini(University of Ferrara)
  2. Giuseppe Mazzotta(University of Calabria)
  3. Francesco Ricca(University of Calabria)
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BibTeX
https://doi.org/10.24963/kr.2026/9

Keywords

  1. null-Knowledge Representation and Reasoning
  2. null-Answer Set Programming with Quantifiers
  3. null-Probabilistic Answer Set Programming
  4. null-probabilistic reasoning
  5. null-Uncertainty in AI

Abstract

Answer Set Programming with Quantifiers (ASP(Q)) extends Answer Set Programming (ASP) by allowing quantification over answer sets.

Although probabilistic extensions to ASP exist, there is no such counterpart for ASP(Q).

In this paper, we close this gap by introducing Inferential Quantified Answer Set Programming (ASP(Q)Inf), an extension of ASP(Q) that supports probabilistic inference over programs with alternating quantifiers, allowing uncertainty at the innermost level.

We demonstrate the modeling capabilities of ASP(Q)Inf, analyze its computational complexity, and present an implementation based on Algebraic Model Counting. An experimental evaluation confirms its effectiveness and practical applicability.