Lazy Explanation-Based Approximation for Probabilistic Logic Programming
This work addresses the computational bottleneck in probabilistic logic programming for researchers and practitioners, offering an incremental improvement in inference speed and accuracy.
The paper tackled the problem of approximate inference in probabilistic logic programming by introducing a lazy approach that uses only the most significant parts of the program, resulting in a fast anytime algorithm that provides hard lower and upper bounds on probabilities and outperforms state-of-the-art methods in experiments.
We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs. It uses only the most significant part of the program when searching for explanations. The result is a fast and anytime approximate inference algorithm which returns hard lower and upper bounds on the exact probability. We experimentally show that this method outperforms state-of-the-art approximate inference.