An overview of the ABC Repair System for Datalog-like Theories

Humans are smart in revising their knowledge and concepts based on observations when they find conflicts. This ability to repair representations is also important for AI agents so that they can represent their environment correctly. This paper gives an overview of the domain-independent ABC system for repairing faulty logical theories by combining three existing techniques: abduction, belief revision and conceptual change. (A) Given an observation, represented as an assertion, and a current theory, abduction adds axioms, or deletes preconditions, which explain that observation by making the corresponding assertion derivable from the expanded theory. (B) Belief revision incorporates a new piece of information which conflicts with the input theory by either deleting old axioms or adding new preconditions to them. (C) Conceptual change uses the reformation algorithm for blocking unwanted proofs or unblocking wanted proofs. The former two techniques change an axiom as a whole, while reformation changes the language in which the theory is written. These three techniques are complementary so they are combined into one system: the ABC repair system, which is capable of repairing logical theories with better result than each individual technique alone and has been applied to applications in multiple domains. Datalog is used as ABC’s underlying logic of theories, but the proposed system has the potential to be adapted to theories in other logics.