Automating Theory Repair in First Order Logic

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.