Automating Theory Repair in First Order Logic

Thomas Wong, Xue Li, Alan Bundy

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

Abstract / Description of output

Automatic theory repair systems help identify and repair faults in a knowledge base, which has useful applications in artificial intelligence such as decision systems. The ABC system is a state-of-the-art implementation of such systems which combines three existing techniques: abduction, belief revision and conceptual change, but with a limitation that it only accepts Datalog logic. To enhance its expressive power, this study extends the ABC system to first-order logic (ABC\_FOL), by augmenting the fault detection module and adding new repair plans to the system. The resultant extended system is able to correctly identify faults and generate sensible repairs across a diverse set of first-order logic examples that cannot be expressed in Datalog logic.
Original languageEnglish
Title of host publicationCognitive AI 2023
PublisherCEUR-WS
Pages1-8
Number of pages8
Publication statusAccepted/In press - 25 Aug 2023
EventCognitive AI 2023 - Bari, Italy
Duration: 13 Nov 202315 Nov 2023
Conference number: 1
https://cognitive-ai.netlify.app/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
ISSN (Electronic)1613-0073

Workshop

WorkshopCognitive AI 2023
Abbreviated titleCogAI 2023
Country/TerritoryItaly
CityBari
Period13/11/2315/11/23
Internet address

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