The frame problem sits at the intersection of artificial intelligence and philosophy, asking how to represent action effects in logical systems without enumerating countless obvious non-effects. In AI's technical formulation, when we paint an object blue or move it to the garden, we expect intuitive outcomes—yet formal logic alone cannot derive that the color change doesn't affect position.

AI researchers like McCarthy & Hayes tackled this by developing frame axioms stating that painting won't change position and moving won't change color. However, this approach scales poorly: with M actions and N properties, systems require nearly MN axioms. The real challenge lies in formalizing the common sense law of inertia—assuming properties remain unchanged unless evidence suggests otherwise.

Classical logic's monotonicity prevents expressing rules with open-ended exceptions, spurring development of non-monotonic reasoning formalisms like circumscription. Though obstacles persist—including the Yale shooting problem and complexities around concurrent actions—the technical frame problem is largely solved for modern logic-based AI systems.

Philosophers, however, seized on the frame problem as revealing deeper epistemological questions about how minds update beliefs efficiently. This broader interpretation explores how cognitive systems determine relevance amid countless possibilities, making it a foundational challenge for understanding human reasoning itself.