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Abstract: Kleinberg (1999) describes a novel procedure for efficient search in a dense hyper-linked environment, such as the world wide web. The procedure exploits information implicit in the links between pages so as to identify patterns of connectivity indicative of “authorative sources”. At a more general level, the trick is to use this second-order link-structure information to rapidly and cheaply identify the knowledge- structures most likely to be relevant given a specific input. I shall argue that Kleinberg’s procedure is suggestive of a new, viable, and neuroscientifically plausible solution to at least (one incarnation of) the so-called “Frame Problem” in cognitive science viz the problem of explaining global abductive inference. More accurately, I shall argue that
Abstract: The frame problem is the problem of how we selectively apply relevant knowledge to particular situations in order to generate practical solutions. Some philosophers have thought that the frame problem can be used to rule out, or argue in favor of, a particular theory of belief states. But this is a mistake. Sentential theories of belief are no better or worse off with respect to the frame problem than are alternative theories of belief, most notably, the “map” theory of belief
Abstract: I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers
Abstract: From its humble origins labeling a technical annoyance for a particular AI formalism, the term "frame problem" has grown to cover issues confronting broader research programs in AI. In philosophy, the term has come to encompass allegedly fundamental, but merely superficially related, objections to computational models of mind in AI and beyond
Abstract: Much research in AI (and cognitive science, more broadly) proceeds on the assumption that there is a difference between being well-informed and being smart. Being well-informed has to do, roughly, with the content of one’s representations--with their truth and the range of subjects they cover. Being smart, on the other hand, has to do with one’s ability to process these representations and with packaging them in a form that allows them to be processed efficiently. The main theoretical concern of artificial intelligence research is to solve "process-and-form" problems: problems with finding processes and representational formats that enable us to understand how a computer could be smart
Abstract: For many of the authors in this volume, this is the second attempt to explore what McCarthy and Hayes (1969) first called the “Frame Problem”. Since the first compendium (Pylyshyn, 1987), nicely summarized here by Ronald Loui, there have been several conferences and books on the topic. Their goals range from providing a clarification of the problem by breaking it down into subproblems (and sometimes declaring the hard subproblems to not be the_ real_ Frame Problem), to providing formal “solutions” to certain aspects of the problem. But more often the message has been that the problem is not solvable except in a piecemeal way in special circumstances by some sort of heuristic approximations. It has sometimes also been said that solving the Frame Problem is not only an unachievable goal, but it is also an unnecessary one since_ humans_ do not solve it either; we simply get along as best we can and deal with the problem of planning in ways that, to use Dennett’s phrase, is “good enough for government work”
Abstract: We humans often respond effectively when faced with novel circumstances. This is because we are able to predict how particular alterations to the world will play out. Philosophers, psychologists, and computational modelers have long favored an account of this process that takes its inspiration from the truth-preserving powers of formal deduction techniques. There is, however, an alternative hypothesis that is better able to account for the human capacity to predict the consequences worldly alterations. This alternative takes its inspiration from the powers of truth preservation exhibited by scale models and leads to a determinate computational solution to the frame problem
