Abstract
The ABC system repairs faulty Datalog theories using a combination of abduction, belief revision and conceptual change via reformation. Abduction and Belief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the language of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones. We apply an algorithm for solving Max-Sat problems, which we call the Partial Max-Sat algorithm, to form this Pareto front.
M. Urbonas was funded by a studentship from the Student Awards Agency Scotland, A. Bundy was funded by EPSRC grant F14R10199 and J. Casanova by an EPSRC CDT in Data Science and a Brainnwave studentship. We are grateful to Joshua Knowles for suggesting this project, and to several anonymous reviewers for suggestions that improved the paper.
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Notes
- 1.
The opposite of the Prolog convention.
- 2.
Although orphans cannot appear in a well-formed Datalog theory, we define them here because they may be created temporarily during the repair process, so must be identified and then eliminated by subsequent repairs.
- 3.
Personal communication from Frank van Harmelen. Based on the LOD-a-lot survey of the Linked Open Data cloud, he estimates that of 23.8 billion unique statements only 565 million could be classified as rules - the rest being facts, i.e., rules make up just under 2% of the total. For more detail, see https://frankvanharmelen.home.blog/2020/07/13/2-makes-all-the-difference-on-the-lod-cloud/ Accessed 14 July 20.
- 4.
Pronounced ‘new \(\mathbb {T}_{Tw}\)’.
- 5.
The details are subject to NDA, so have been anonymised.
- 6.
Plus our development example 1.
- 7.
Space limitations prohibit us from giving the axioms for each of these theories, except for the Tweety example (1). The remaining theories can be found online at https://github.com/MariusUrbonas/AutomatedPruningMechanismForTheoryRepairs.
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Urbonas, M., Bundy, A., Casanova, J., Li, X. (2020). The Use of Max-Sat for Optimal Choice of Automated Theory Repairs. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVII. SGAI 2020. Lecture Notes in Computer Science(), vol 12498. Springer, Cham. https://doi.org/10.1007/978-3-030-63799-6_4
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