Logikaiszabályalapú

Logikaiszabályalapú is a Hungarian phrase that literally translates to “logic rule‑based.” It refers to a class of knowledge‑based systems and decision engines that use formal logical rules to represent expert knowledge, infer new information, and control behavior. The core idea is to encode the relationships among concepts in a domain as a set of declarative statements—often in the form of “if‑then” implications—rather than relying on statistical patterns or black‑box models.

Historically, logic‑rule‑based systems emerged in the 1960s and 1970s with early expert systems such as MYCIN, which used production rules to diagnose bacterial infections. These systems were built on a production‑rule engine that matched current facts against the antecedents of rules and executed the consequent actions. Over time, rule engines evolved to support more expressive logics, including predicate logic, modal logic, and ontological frameworks. Modern rule‑based platforms—such as Drools, CLIPS, and SWI‑Prolog—provide scalable inference engines, decision tables, and support for constraint logic programming.

Applications of logic‑rule‑based systems remain widespread in domains where transparency and explainability are paramount. Common use cases include configuration management, business rule automation, compliance monitoring, fraud detection, and healthcare decision support. Because the reasoning process is explicit, auditors and users can trace how a particular conclusion was derived, which is especially valuable in regulated industries.

In contrast to machine‑learning approaches that learn statistical patterns from data, logic‑rule‑based systems rely on human‑crafted knowledge. This makes them easier to maintain for knowledge domains that change gradually, but also limits their ability to discover novel patterns automatically. Contemporary AI practice often blends rule‑based inference with machine‑learning components, producing hybrid models that balance interpretability with predictive performance.