# Using ASP(Q) to Handle Inconsistent Prioritized Data

arXiv: https://arxiv.org/abs/2604.21603 · HTML: https://mlsanity.com/paper/2604.21603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Planning with Incomplete Information in Quantified Answer Set Programming](https://mlsanity.com/paper/2108.06405/) (2021)
- [Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation](https://mlsanity.com/paper/2003.05746/) (2020)
- [Beyond NP: Quantifying over Answer Sets](https://mlsanity.com/paper/1907.09559/) (2019)
- [A SAT-based System for Consistent Query Answering](https://mlsanity.com/paper/1905.02828/) (2019)
- [Inconsistency Handling in Prioritized Databases with Universal Constraints: Complexity Analysis and Links with Active Integrity Constraints](https://mlsanity.com/paper/2306.03523/) (2023)
- [An efficient solver for ASP(Q)](https://mlsanity.com/paper/2305.10021/) (2023)
- [A Rule-Based Approach to Specifying Preferences over Conflicting Facts and Querying Inconsistent Knowledge Bases](https://mlsanity.com/paper/2508.07742/) (2025)

## Abstract (arXiv)

We explore the use of answer set programming (ASP) and its extension with quantifiers, ASP(Q), for inconsistency-tolerant querying of prioritized data, where a priority relation between conflicting facts is exploited to define three notions of optimal repairs (Pareto-, globally- and completion-optimal). We consider the variants of three well-known semantics (AR, brave and IAR) that use these optimal repairs, and for which query answering is in the first or second level of the polynomial hierarchy for a large class of logical theories. Notably, this paper presents the first implementation of globally-optimal repair-based semantics, as well as the first implementation of the grounded semantics, which is a tractable under-approximation of all these optimal repair-based semantics. Our experimental evaluation sheds light on the feasibility of computing answers under globally-optimal repair semantics and the impact of adopting different semantics, approximations, and encodings.