Large-scale cost-based abduction in full-fledged first-order predicate logic with cutting plane inference

Abduction is inference to the best explanation. Abduction has long been studied intensively in a wide range of contexts, from artificial intelligence research to cognitive science. While recent advances in large-scale knowledge acquisition warrant applying abduction with large knowledge bases to real-life problems, as of yet no existing approach to abduction has achieved both the efficiency and formal expressiveness necessary to be a practical solution for large-scale reasoning on real-life problems. The contributions of our work are the following: (i) we reformulate abduction as an Integer Linear Programming (ILP) optimization problem, providing full support for first-order predicate logic (FOPL); (ii) we employ Cutting Plane Inference, which is an iterative optimization strategy developed in Operations Research for making abductive reasoning in full-fledged FOPL tractable, showing its efficiency on a real-life dataset; (iii) the abductive inference engine presented in this paper is made publicly available.