Framework for benchmarking rule-based inference engines

Abstract

Rule-based systems constitute the state of the art solutions in the area of artificial intelligence. They provide fast, human readable and self explanatory mechanism for encoding knowledge. Due to large popularity of rules, dozens of inference engines were developed over last few decades. They differ in the reasoning efficiency depending on many factors such as model characteristics or deployment platform. Therefore, picking a reasoning engine that best fits the requirement of the system becomes a non-trivial task. The primary objective of the work presented in this paper was to provide a fully automated framework for bench-marking rule-based reasoning engines.