The Frame Problem (2004) (plato.stanford.edu)

The frame problem, originating in logic-based artificial intelligence (AI), addresses how to represent the consequences of actions without unnecessarily articulating numerous non-effects. While AI researchers have developed solutions to this technical challenge—such as non-monotonic reasoning formalisms—the problem has drawn philosophical interest as it raises broader questions about cognitive processes, particularly how entities, like robots, can efficiently update their beliefs based on relevant actions without being overwhelmed by irrelevant data. This intersection between AI and philosophy highlights the implications of decision-making for intelligent systems. Specifically, the nuances of the frame problem underscore both a computational challenge—how to limit the range of inquiry into non-effects when updating beliefs—and an epistemological inquiry into the nature of relevance in cognition. As researchers explore ways to streamline belief updates while ensuring accuracy—using strategies like the "sleeping dog" approach—the discourse around the frame problem continues to evolve, influencing both AI development and our understanding of human-like reasoning.