Rammeproblem

Rammeproblem, often referred to as the frame problem in AI and knowledge representation, denotes the difficulty of modeling the effects of actions in a dynamic environment without needing to enumerate all facts that do not change. When an action occurs, only a limited subset of properties typically changes, while the rest remain true. Without a compact formulation, reasoning systems would be forced to generate and manage an enormous number of frame axioms to capture non-effects.

The term emerges from AI research in the late 1960s and early 1970s, notably in discussions by John McCarthy and Patrick Hayes about how to represent common-sense reasoning about action. A classic illustration uses simple domains such as blocks world or shooting scenarios to show how many attributes must be assumed to persist after an action.

Formal approaches address rammeproblemet by introducing representations that encode inertia or persistence. In situation calculus, successor state axioms describe how each fluent changes or stays the same under actions, avoiding explicit frame conditions. Other methods include circumscription, default logic, fluent calculus, and event calculus. These formalisms aim to keep reasoning tractable while preserving correctness with respect to the effects of actions.

Significance and applications are broad: planning, robotics, and knowledge representation all confront rammeproblemet when designing systems that can infer outcomes of actions in real time. The problem also intersects with non-monotonic reasoning, where conclusions may be revised as new information arrives. While the term is primarily used in AI literature, its core idea—efficiently representing change without listing all non-changes—remains central to contemporary automated reasoning.