Declarative Rules and Rule-Based Systems

Starting with the premise that rule-based systems still have their place in AI toolkit, we explore different ways of implementing such systems. We find that some of the criticisms towards rule-based systems, namely their large number of rules and high maintenance cost can be addressed by using declarative style programming and a shift to a higher level of abstraction from rules to meta-rules, operating on the structure of data rather than its contents. We use OpenNARS 4 as example implementation to demonstrate advantages of this approach but the results are generally applicable to other rule-based systems.