Despite modern advancements in ML, rules engines are still preferred in some scenarios as their execution tends to be deterministic.
They have different heuristics for conflict resolution when more than one rule can be fired. Once you define the strategy, it will be consistent throughout the entire lifecycle.
Once the network is compiled, it ensures that previously matched patterns are not recomputed, which increases performance.
Yeah modern ML is not really at all comparable and they're more complementary than modern approaches replacing rules. All these agent frameworks and platforms cropping up will be using things like rules, workflow DAG models and so on as the execution engine with LLMs embedded as steps and/or to construct a workflow.
Likewise either with knowledge graphs or using LLMs to generate possible predicates and constraints to run against a rule engine or backwards chain through facts is a way to minimize hallucinations of generative models.
They have different heuristics for conflict resolution when more than one rule can be fired. Once you define the strategy, it will be consistent throughout the entire lifecycle.
Once the network is compiled, it ensures that previously matched patterns are not recomputed, which increases performance.