ruledriven

Rule-driven, or ruledriven, describes a design or process in which behavior is governed by a defined set of explicit rules rather than learned patterns or implicit models. In this sense, system actions are determined by declarative rules that can be authored, reviewed, and updated by humans.

Key characteristics include the use of a rule base or knowledge base, an inference engine, and working memory to track state. Rules are usually expressed in a form such as if-then statements or decision tables. The inference engine applies rules, often using forward chaining to derive conclusions or backward chaining to justify goals. Conflict resolution mechanisms determine which rules fire when multiple rules apply.

Rule-driven approaches are common in software engineering, business process management, and knowledge-based systems. They underpin business rule management systems and expert systems, where domains require auditable decision logic, strict compliance, or rapid modification of behavior without changing underlying software. Popular examples include rule engines and decision tables used to encode eligibility criteria, routing logic, pricing rules, and regulatory checks.

Advantages of ruledriven systems include transparency, explainability, and the ability for business stakeholders to modify logic without deep programming. They support traceability and regulatory compliance. Limitations include maintenance overhead for large rule sets, potential rule conflicts or redundancy, and scalability challenges as rules evolve. In fast-changing data environments, rules must be carefully managed to avoid brittleness and ensure consistency with evolving policies.

Rule-driven design is often contrasted with data-driven or machine learning approaches; in practice, many systems combine both paradigms, applying rules for governance and safety while leveraging data-driven models for predictive tasks.