An Efficient Reasoning Method on Logic Programming using Partial Evaluation in Vector Spaces
Hien D Nguyen, Chiaki Sakama, Taisuke Sato, and Katsumi Inoue
Journal of Logic and Computation, vol.31(5), July 2021, pages 1298-1316, 2021.
In this paper, we introduce methods of encoding propositional logic programs in vector spaces.
Interpretations are represented by vectors and programs are represented by matrices.
The least model of a definite program is computed by multiplying an interpretation vector and a program matrix.
To optimize computation in vector spaces, we provide a method of partial evaluation of programs using linear algebra.
Partial evaluation is done by unfolding rules in a program, and it is realized in a vector space by multiplying
program matrices. We perform experiments using artificial data and real data, and show that partial evaluation
has the potential for realizing efficient computation of huge scale of programs in vector spaces.