Linear Algebraic Computation of Propositional Horn Abduction
Tuan Nguyen Quoc, Katsumi Inoue, and Chiaki Sakama
Proceedings of IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 240-247.
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.
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