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Researchers explore ASP(Q) for handling inconsistent prioritized data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method using answer set programming with quantifiers (ASP(Q)) to handle inconsistent prioritized data. This approach allows for querying data where conflicting facts have defined priority levels, leading to three types of optimal repairs: Pareto-, globally-, and completion-optimal. The paper introduces the first implementation of globally-optimal repair-based semantics and a tractable under-approximation called grounded semantics, with experimental results demonstrating their feasibility and impact. AI

IMPACT Introduces novel computational techniques for managing complex data, potentially improving AI reasoning with imperfect information.

RANK_REASON This is a research paper detailing a new computational method for handling data inconsistencies. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers explore ASP(Q) for handling inconsistent prioritized data

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This is a research paper detailing a new computational method for handling data inconsistencies. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Meghyn Bienvenu, Camille Bourgaux, Robin Jean, Giuseppe Mazzotta ·

    Using ASP(Q) to Handle Inconsistent Prioritized Data

    arXiv:2604.21603v2 Announce Type: replace-cross Abstract: 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 de…

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