Benchmark based Vitality of Axioms and Preconditions for Datalog Theory Repair

Xue Li*, Alan Bundy, Ricky Zhu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In general, axioms a logical theory are not equal in terms of their informational value (IV), and neither are the preconditions in a logical rule. Measuring IV is crucial, particularly in automated repair systems, e.g., the Abduction, Belief Revision and Conceptual Change (ABC) repair system \citep{li2022overview}, because when there are multiple repairs, the one that changes the item of least IV is preferred. However, quantifying IV is challenging. Given a benchmark, we
evaluate the IV from the perspective of how much an axiom/precondition supports the benchmark, which is quantitatively defined as the {\em vitality} of axioms and preconditions. The bigger contribution of an axiom/precondition in supporting the benchmark, the more information it conveys, so the bigger its vitality is. Our evaluation shows that the ABC repair system finds the best-repaired theories with smaller search space by only changing the least vital axioms/preconditions, as shown by our evaluation.
Original languageEnglish
Title of host publicationProceedings of the Tenth Annual Conference on Advances in Cognitive Systems
Number of pages16
Publication statusAccepted/In press - 3 Nov 2022
EventThe Tenth Annual Conference on Advances in Cognitive Systems, 2022 - Arlington, United States
Duration: 19 Nov 202222 Nov 2022
Conference number: 10
http://www.cogsys.org/conference/2022/

Conference

ConferenceThe Tenth Annual Conference on Advances in Cognitive Systems, 2022
Abbreviated titleACS 2022
Country/TerritoryUnited States
CityArlington
Period19/11/2222/11/22
Internet address

Keywords

  • Entrechment
  • Theory repair
  • Preferences

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