Researchers explore ASP(Q) for handling inconsistent prioritized data

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]

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• arXiv
• ASP(Q)
• Camille Bourgaux

• paper
• other

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