Generating Random Logic Programs Using Constraint Programming

TL;DR

This paper introduces a new method using constraint programming to generate diverse random logic and probabilistic logic programs, enabling comprehensive evaluation of inference algorithms.

Contribution

The paper presents a novel constraint-based approach for generating random logic programs with controllable independence structures, expanding beyond propositional limitations.

Findings

Model scales effectively with parameters

Enables extensive algorithm comparison

Provides detailed insights into algorithm strengths

Abstract

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.