The Frame Problem, Then and Now

My collaboration with John McCarthy started in 1984. He was interested then in what he called the commonsense law of inertia. That idea is related to actions, such as moving an object to a different location, or, for instance, toggling a light switch. According to this law, whatever we know about the state of affairs before executing an action can be presumed, by default, to hold after the action as well. Formalizing this default would resolve the difficulty known in AI as the frame problem. Reasoning with defaults is nonmonotonic. John proposed a solution to the frame problem based on the method of nonmonotonic reasoning that he called circumscription [1]. And then something unpleasant happened. Two researchers from Yale University discovered that John’s proposed solution was incorrect [2]. Their counterexample involved the actions of loading a gun and shooting, and it became known as the “Yale Shooting Scenario.” Twenty years later their paper received the AAAI Classic Paper Award [3]. But that was not all. Automated reasoning is notoriously difficult, and the presence of defaults adds yet another level of complexity. Even assuming that the problem with Yale Shooting is resolved, was there any hope, one could ask, that the commonsense law of inertia would ever become part of usable software? So John’s proposal seemed unsound and non-implementable. It also seemed unnecessary, because other researchers have proposed approaches to the frame problem that did not require nonmonotonic reasoning [4, 5, 6]. There were all indications that his project just wouldn’t fly. But history showed otherwise. It was destined to fly, and, in fact, to fly quite high: in outer space. I’d like to tell you about a program written years later by a group of computer scientists who continued John’s research on nonmonotonic reasoning in collaboration with engineers from United Space