This is my Masters dissertation about the frame problem of Artificial Intelligence. The core claim of this work is that the problem (conceived as a problem about relevance) is neither computational nor representational in nature. It plagues every contemporary explanatory framework. In particular, one does not simply get rid of the frame problem by (e.g.) adopting a 4EA perspective.
Abstract
Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of cir- cumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of ex- plaining how human cognition efficiently acknowledges what is relevant from what is not in each context, has been adopted as a guide. By using it, we’ve been able to des- cribe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem, as well as the reasons for its persistence. In the second and third chapters, the pro- blem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research.
Language
Portuguese