Linear Algebraic Computation of Propositional Horn Abduction
Tuan Quoc Nguyen, Katsumi Inoue, Chiaki Sakama · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
Linear algebraic characterization of logic programs has been investigated to perform logical inference in large-scale knowledge bases and has gained encouraging results. In this paper, we further extend the linear algebraic characterization in abductive reasoning by exploiting the transpose of the program matrix. Then we propose an efficient exhaustive search strategy, which combines the flexibility and robustness of numerical computation with the compactness and efficiency of set operations, in order to compute solutions of abductive Horn propositional tasks. Experimental results demonstrate that our method is competitive with conflict-driven techniques and has the potential to speed up on parallel computing platforms.