Partial Evaluation of Logic Programs in Vector Spaces
This work addresses the challenge of scaling logic program computations for applications in AI or data processing, but it appears incremental as it builds on existing vector space encodings with a focus on optimization.
The paper tackles the problem of encoding propositional logic programs in vector spaces to compute least models efficiently, and shows through experiments on randomly generated programs that partial evaluation using linear algebra has potential for efficient computation at large scales.
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 randomly generated programs and show that partial evaluation has potential for realizing efficient computation in huge scale of programs.