Frames in the space of situations (research note)
Artificial Intelligence
Features and fluents (vol. 1): the representation of knowledge about dynamical systems
Features and fluents (vol. 1): the representation of knowledge about dynamical systems
Artificial Intelligence
Preferential Semantics for Causal Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A causal theory of ramifications and qualifications
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Systems theory: melding the AI and simulation perspectives
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
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The Principle of Minimal Change is prevalent in various guises throughout the development of areas such as reasoning about action, belief change and nonmonotonic reasoning. Recent literature has witnessed the proposal of several theories of action that adopt an explicit representation of causality. It is claimed that an explicit notion of causality is able to deal with the frame problem in a manner not possible with traditional approaches based on minimal change. However, such claims remain untested by all but representative examples. It is our purpose here to objectively test these claims in an abstract sense; to determine whether an explicit representation of causality is capable of providing something that the Principle of Minimal Change is unable to capture. Working towards this end, we provide a precise characterisation of the limit of applicability of minimal change.