Rule-Based Perception
Rule-based perception is an artificial intelligence approach where perception tasks, such as object recognition or scene understanding, are performed using a set of predefined logical rules or heuristics. It involves encoding expert knowledge into explicit rules that guide how sensory data is interpreted, often in structured environments where conditions are predictable. This method contrasts with data-driven approaches like machine learning, relying on symbolic reasoning rather than statistical patterns.
Developers should learn rule-based perception when working on systems that require high interpretability, operate in well-defined domains with clear constraints, or need to enforce strict safety or regulatory rules, such as in industrial automation, robotics, or expert systems. It is particularly useful in scenarios where data is scarce, real-time performance is critical, or decisions must be explainable, such as in medical diagnosis or autonomous vehicle perception under controlled conditions.