Why Dreyfus’ Frame Problem Argument Cannot Justify Anti-Representational AI

Why Dreyfus’ Frame Problem Argument Cannot Justify Anti- Representational AI Nancy Salay (salay@queensu.ca) Department of Philosophy, Watson Hall 309 Queen‘s University, Kingston, ON K7L 3N6 Abstract disembodied cognitive models will not work, and this conclusion needs to be heard. By disentangling the ideas of embodiment and representation, at least with respect to Dreyfus‘ frame problem argument, the real locus of the general polemic between traditional computational- representational cognitive science and the more recent embodied approaches is revealed. From this, I hope that productive debate will ensue. The paper proceeds in the following way: in section I, I describe and distinguish the logical version of the frame problem and the philosophical one that remains unsolved; in section II, I rehearse Dreyfus‘ negative argument, what I‘ll be calling his frame problem argument; in section III, I highlight some key points from Dreyfus‘ positive account of a Heideggerian alternative; in section IV, I make my case against the anti-representational conclusions Dreyfus wants to draw from his frame problem argument; and, in section V, I speculate on the nature of an embodiment-directed AI not hampered by the frame problem. Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one that does not appeal to representations. I argue that Dreyfus is too hasty in his condemnation of all representational views; the argument he provides licenses only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the cognitive playing field so closely that one is left wondering how his Heideggerian alternative could ever provide a foundation explanatorily robust enough for a theory of cognition. As a consequence, he dilutes the force of his legitimate conclusion, that disembodied cognitive models will not work, and this conclusion needs to be heard. By disentangling the ideas of embodiment and representation, at least with respect to Dreyfus‘ frame problem argument, the real locus of the general polemic between traditional computational/representational cognitive science and the more recent embodied approaches is revealed. From this, I hope that productive debate will ensue. I: The Frame Problem Keywords: representation; cognition, embodiment; embodied; enactive; Dreyfus; Heidegger; frame problem Introduction Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan, 1997; Thielscher, 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one that does not appeal to representations. But, as I shall argue, Dreyfus is too hasty in his condemnation of all representational views; the argument he provides licenses only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the cognitive playing field so closely that one is left wondering how his Heideggerian alternative could ever provide a foundation explanatorily robust enough for a theory of cognition. As a consequence, he dilutes the force of his legitimate conclusion, that The first version of the frame problem (McCarthy & Hayes, 1969), the logical frame problem, refers to a technical difficulty that emerged out of the logicist framework within which early models of AI were conceived. One necessary feature of any AI system is the ability to handle change. For a logicist AI system, ‗to handle change‘ means to make correct inferences about which features in its world model to update and which to ignore when something in the environment changes. For example, if a ball is moved from one box to another, the system will need to update the ball‘s location and the empty/full status of the respective boxes, but it ought to leave its representations of the other items in the room alone, e.g. the fridge will remain white, the stove will remain off, and so on. A logicist system, however, will not infer anything unless it has a rule telling it to do so; thus, the system will need explicit rules, frame axioms, which dictate both what will change and what will stay the same for a given action. But not only is creating a comprehensive set of frame axioms impossible in a world-scale system – more on this in a moment – even were it doable, the resulting knowledge base would be prohibitively large, from an inferential perspective; for M possible actions and N attributes, M x N frame axioms would be needed. The