Cognitive wheels: the frame problem of AI

Once upon a time there was a robot, named R1 by its creators. Its only task was to fend for itself. One day its designers arranged for it to learn that its spare battery, its precious energy supply, was locked in a room with a time bomb set to go off soon. R1 located the room, and the key to the door, and formulated a plan to rescue its battery. There was a wagon in the room, and the battery was on the wagon, and R1 hypothesized that a certain action which it called PULLOUT (Wagon, Room, t) would result in the battery being removed from the room. Straightaway it acted, and did succeed in getting the battery out of the room before the bomb went off. Unfortunately, however, the bomb was also on the wagon. R1 knew that the bomb was on the wagon in the room, but didn’t realize that pulling the wagon would bring the bomb out along with the battery. Poor R1 had missed that obvious implication of its planned act. Back to the drawing board. ‘The solution is obvious,’ said the designers. ‘Our next robot must be made to recognize not just the intended implications of its acts, but also the implications about their side-effects, by deducing these implications from the descriptions it uses in formulating its plans.’ They called their next model, the robot-deducer, R1D1. They placed R1D1 in much the same predicament that R1 had succumbed to, and as it too hit upon the idea of PULLOUT (Wagon, Room, t) it began, as designed, to consider the implications of such a course of action. It had just finished deducing that pulling the wagon out of the room would not change the colour of the room’s walls, and was embarking on a proof of the further implication that pulling the wagon out would cause its wheels to turn more revolutions than there were wheels on the wagon—when the bomb exploded. Back to the drawing board. ‘We must teach it the difference between relevant implications and irrelevant implications,’ said the designers, ‘and teach it to ignore the irrelevant ones.’ So they developed a method of tagging implications as either relevant or irrelevant to the project at hand, and installed the method in their next model, the robot-relevant-deducer, or R2D1 for short. When they subjected R2D1 to the test that had so unequivocally selected its ancestors for extinction, they were surprised to see it sitting, Hamlet-like, outside the room containing the ticking bomb, the native hue of its resolution sicklied o’er with the pale cast of thought, as Shakespeare (and more recently Fodor) has aptly put it. ‘Do something!’ they yelled at it. ‘I am,’ it retorted. ‘I’m busily ignoring some thousands of implications I have determined to be irrelevant. Just as soon as I find an irrelevant implication, I put it on the list of those I must ignore, and...’ the bomb went off. All these robots suffer from the frame problem. If there is ever to be a robot with the fabled perspicacity and real-time adroitness of R2D2, robot-designers must solve the frame problem. It appears at first to be at best an annoying technical embarrassment in robotics, or merely a curious puzzle for the bemusement of people working in Artificial Intelligence (AI). I think, on the contrary, that it is a new, deep epistemological problem—accessible in principle but unnoticed by generations of philosophers—brought to light by the novel methods of AI, and still far from being solved. Many people in AI have come to have a similarly high regard for the seriousness of the frame problem. As one researcher has quipped, ‘We have given up the goal of designing an intelligent robot, and turned to the task of designing a gun that will destroy any intelligent robot that anyone else designs!’ I will try here to present an elementary, non-technical, philosophical introduction to the frame problem, and show why it is so interesting. I have no solution to offer, or even any original suggestions for where a solution might lie. It is hard enough, I have discovered, just to say clearly what the frame problem is—and is not. In fact, there is less than perfect agreement in usage within the AI research community. McCarthy and Hayes, who coined the term, use it to refer to a particular, narrowly conceived problem about representation that arises only for certain strategies for dealing with a broader problem about real-time planning systems. Others call this broader problem the frame problem-‘the whole pudding,’ as Hayes has called it (personal correspondence)—and this may not be mere terminological sloppiness. If ‘solutions’ to the narrowly conceived problem have the effect of driving a (deeper) difficulty into some other quarter of the broad problem, we might better reserve the title for this hard-to-corner difficulty. With apologies to McCarthy and Hayes for