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- Kevin B. Korb (1998). The Frame Problem: An AI Fairy Tale. Minds and Machines 8 (3):317-351.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.Reading list | Discuss | Edit | Categorize |My bibliography
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The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by the data — how are we to restrict the space of inductive hypotheses and choose effectively some rules that will probably perform well on future examples? We analyze if and how this problem is approached in standard accounts of induction and show the difficulties that are present. Finally, we suggest that the explanation-based learning approach and related methods of knowledge intensive induction could be, if not a solution, at least a tool for solving some of these problems.
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| | Other links: springerlink.com | Scholar | At my library | More options ... The frame problem is widely reputed among philosophers to be one of the deepest and most difficult problems of cognitive science. This paper discusses three recent attempts to display this problem: Dennett's problem of ignoring obviously irrelevant knowledge, Haugeland's problem of efficiently keeping track of salient side effects, and Fodor's problem of avoiding the use of kooky concepts. In a negative vein, it is argued that these problems bear nothing but a superficial similarity to the frame problem of AI, so that they do not provide reasons to disparage standard attempts to solve it. More positively, it is argued that these problems are easily solved by slight variations on familiar AI themes. Finally, some discussion is devoted to more difficult problems confronting AI.
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| | Other links: springerlink.com jstor.org | Scholar | At my library | More options ... The chapters in this book have evolved from talks originally presented at The First International Workshop on Human and Machine Cognition.
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| | Scholar | At my library | More options ... A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
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| | Scholar | At my library | More options ... I have discussed the frame problem and the Turing test at length, but I have not
attempted to spell out what I think the implications of the frame problem ...
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| | Scholar | At my library | More options ... 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.
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| | Other links: springerlink.com ingentaconnect.com jstor.org | Scholar | At my library | More options ... Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: jstor.org blackwell-synergy.com ingentaconnect.com dx.doi.org | Scholar | At my library | More options ...
| | Other links: jstor.org blackwell-synergy.com ingentaconnect.com dx.doi.org | Scholar | At my library | More options ... 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”.
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| | Other links: ruccs.rutgers.edu portal.acm.org | Scholar | At my library | More options ... Abstract The frame problem is a problem that arises when an agent attempts to assess the consequences of future behaviour. Strictly, it is a problem of modelling that arises during planning. The problem arises because many of the possible consequences of a planned action are not really relevant to the decision whether to perform the action. The frame problem is typical of the classical approach to artificial intelligence, but it is evident that animals do not suffer from this problem. In this paper it is suggested that animals can circumvent the frame problem because their decision?making architecture is very different from that traditionally used in artificial intelligence.
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| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: informaworld.com tandfonline.com dx.doi.org | Scholar | At my library | More options ...
| | Other links: informaworld.com tandfonline.com dx.doi.org | Scholar | At my library | More options ... The frame problem is a problem in artificial intelligence that a number of philosophers have claimed has philosophical relevance. The structure of this paper is as follows: (1) An account of the frame problem is given; (2) The frame problem is distinguished from related problems; (3) The main strategies for dealing with the frame problem are outlined; (4) A difference between commonsense reasoning and prediction using a scientific theory is argued for; (5) Some implications for the..
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| | Scholar | At my library | More options ... Discussion of Kevin B. Korb, The frame problem: An AI fairy tale
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