## 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.

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### References

- Brooks, R. (1991), ‘Intelligence without Representation’,
*Artificial Intelligence,*47, pp 139–159.Google Scholar - Brown, F.M. (ed.) (1987),
*The Frame Problem in Artificial Intelligence,*Morgan Kaufmann.Google Scholar - Buck, R.C. (1963), ‘Do Reflexive Predictions Pose Special Problems for the Social Scientist?’,
*Philosophy of Science*, 30, pp 359–374.Google Scholar - Carnap, R. (1962),
*Logical Foundations of Probability,*second edition, University of Chicago.Google Scholar - Cherniak, C. (1986),
*Minimal Rationality,*Cambridge, Mass.: MIT Press.Google Scholar - Dennett, D. (1984), ‘Cognitive Wheels: The Frame Problem of Artificial Intelligence’, in C. Hookaway (ed.)
*Minds, Machines and Evolution,*Cambridge University. Reprinted in Pylyshyn (1987).Google Scholar - Dietrich, E. and Fields, C. (1996), ‘The Role of the Frame Problem in Fodor's Modularity Thesis: A Case Study of Rationalist Cognitive Science’, in K. M. Ford and Z. W Pylyshyn, (eds.)
*The Robot's Dilemma Revisited:*Norwood, N. J.: Ablex, pp 9–24.Google Scholar - Dreyfus, H. and Dreyfus, S. (1987), ‘How to Stop Worrying about the Frame Problem even though it's Computationally Insoluble’, in Z. W. Pylyshyn (ed),
*The Robot's Dilemma*Norwood, N. J.: Ablex, pp. 95–111.Google Scholar - Fetzer, J. (1991b), ‘The Frame Problem: Artificial Intelligence Meets David Hume’, In K. M. Ford and P. J Hayes (eds.)
*Reasoning Agents in a Dynamic World*Greenwich, CT: JAI Press pp. 55–69.Google Scholar - Fetzer, J. (1991b), ‘Artificial Intelligence Meets David Hume: A Response to Patrick Hayes’, in K. M Ford and P. J. Hayes (eds.)
*Reasoning Agents in a Dynamic World*Greenwich, CT: JAI Press pp. 77–85.Google Scholar - Fetzer, J. (1993), ‘Philosophy Unframed’,
*Psycoloquy*4,(33).Google Scholar - Ford, K.M. and Hayes, P.J. (eds.) (1991a),
*Reasoning Agents in a Dynamic World: The Frame Problem*. Greenwich, CT: JAI Press.Google Scholar - Ford, K.M. and Hayes, P.J. (1991b), ‘Framing the Problem’, in K. M Ford and P. J Hayes (eds.)
*Reasoning Agents in a Dynamic World*Greenwich, CT: JAI Press pp. ix-xiv.Google Scholar - Ford, K.M. and Pylyshyn, Z.W. (1996),
*The Robot's Dilemma Revisited*, Norwood, N.J.: Ablex.Google Scholar - Freeman, W. (1992), ‘Framing Is a Dynamic Process’,
*Psycoloquy,*3(62).Google Scholar - Georgeff, M.P. and Wallace, C.S. (1984) ‘A General Selection Criterion for Inductive Inference’,
*European Conference on Artificial Intelligence,*6, pp. 473–482.Google Scholar - Gillies, D. (1990), ‘The Turing-Good Weight of Evidence Function and Popper's Measure of the Severity of a Test’,
*British Journal for the Philosophy of Science*41, pp 143–146.Google Scholar - Gillies, D. (1996),
*Artificial Intelligence and Scientific Method*, Oxford: Oxford University Press.Google Scholar - Goldberg, D.E. (1989),
*Genetic Algorithms in Search, Optimization, and Machine Learning)*, Addison-Wesley.Google Scholar - Grice, H.P. (1975), ‘Logic and Conversation’, in P. Cole and J.P. Morgan (eds.)
*Syntax and Semantics, Volume 3: Speech Acts,*pp. 41–58) New York: Seminar Press.Google Scholar - Hanks, S. and D. McDermott (1986), ‘Default Reasoning, Nonmonotonic Logic, and the Frame Problem’, in T. Kehler, S. Rosenschein, R. Filman and P. Patel-Schneider (eds.)
*Proceedings of the Fifth National Conference on Artificial Intelligence,*pp. 328–333, Morgan Kaufmann.Google Scholar - Harnad, S. (1993), ‘Problems, Problems: The Frame Problem as a Symptom of the Symbol Grounding Problem’,
*Psycoloquy*4(34). http://cogsci.ecs.soton.ac.uk/harnard/psyc.htmlGoogle Scholar - Haugeland, J. (1987), ‘An Overview of the Frame Problem’, in Z.W. Pylyshyn
*The Robot's Dilemma*Norwood, N.J.: Ablex, pp. 77–93.Google Scholar - Hayes, P. (1987), ‘What the Frame Problem Is and Isn't’, in Z. W. Pylyshyn (ed),
*The Robot's Dilemma*Norwood, N. J.: Ablex, pp. 123–137.Google Scholar - Hayes, P. (1991), ‘Commentary on’ The Frame Problem: Artificial Intelligence Meets David Hume', in in K. M Ford and P. J Hayes (eds.)
*Reasoning Agents in a Dynamic World*Greenwich, CT: JAI Press, pp. 71–76.Google Scholar - Hayes, P. and Ford, K. M. (1993), ‘Modeling our Adaptive Intelligence, Not God's’,
*Psycoloquy,*4(42). http://cogsci.ecs.soton.ac.uk/harnard/psyc.htmlGoogle Scholar - Heckerman, D. and Geiger, D. (1995), ‘Likelihoods and Priors for Learning Bayesian Networks’,
*Neural Information Processing Systems 1995 Workshop on Learning in Bayesian Networks and Other Graphical Models, December 1995, Veil, Colorado*.Google Scholar - Heckerman, D., Mamdani, A. and Wellman, M. P. (1995), ‘Real-world Applications of Bayesian Networks’,
*Communications of the ACM*38 (March), p 24–26.Google Scholar - Howson, C. and Urbach, P. (1993),
*Scientific Reasoning: The Bayesian Approach,*second edition, Chicago: Open Court.Google Scholar - Hume, D. (1748/1975)
*An Enquiry Concerning Human Understanding*, ed. L. A. Selby-Bigge, revised by P. H. Nidditch, Oxford University.Google Scholar - Israel, D. (1985), ‘A Short Companion to the Naive Physics Manifesto’, in J.R. Hobbs and R.C. Moore (eds.)
*Formal Theories of the Commonsense World*, Norwood, N. J.: Ablex. pp. 427–447.Google Scholar - Korb, K.B. (1995), ‘Inductive Learning and Defeasible Inference’,
*Journal of Experimental and Theoretical Artificial Intelligence*7, pp. 291–324.Google Scholar - Korb, K.B. (1998), ‘Probabilistic Causal Structure’, forthcoming in H. Sankey (ed.),
*Causation and Natural Laws: Australasian Studies in the History and Philosophy of Science,*Kluwer.Google Scholar - Korb, K.B. and Wallace, C. S. (1997) ‘In Search of the Philosopher's Stone: Remarks on Humphreys and Freedman's Critique of Causal Discovery’,
*British Journal for the Philosophy of Science*48, pp. 543–553.Google Scholar - Langley, P. Simon, H. A. and Bradshaw, G. L. (1990), ‘Heuristics for Empirical Discovery,’ in Shavlik and Diettrich (eds.),
*Readings in Machine Learning,*Morgan Kaufmann.Google Scholar - Langley, P. Simon, H. A. Bradshaw, G. L. and Zytow, J. M. (1987),
*Scientific Discovery,*MIT.Google Scholar - McCarthy, J. and Hayes, P. J. (1969), ‘Some Philosophical Problems from the Standpoint of Artificial Intelligence’, in B. Meltzer and D. Michie (eds.),
*Machine Intelligence*4, pp. 463–502, Edinburgh University Press. Reprinted in M.L. Ginsberg (ed.)*Readings in Nonmonotonic Reasoning,*pp. 26–45, Morgan Kaufmann.Google Scholar - McDermott, D. (1987a), ‘We've Been Framed: Or, Why AI Is Innocent of the Frame Problem’, in Z. W. Pylyshyn (ed),
*The Robot's Dilemma*Norwood, N. J.: Ablex, pp. 113–122.Google Scholar - McDermott, D. (1987b), ‘AI, Logic and the Frame Problem’, in F. M. Brown
*The Frame Problem in Artificial Intelligence,*Morgan Kaufmann, pp. 105–118.Google Scholar - McDermott, D. (1987c), ‘A Critique of Pure Reason’,
*Computational Intelligence*3, pp. 151–160.Google Scholar - Mitchell, T.M. (1977), ‘Version Spaces: An Approach to Rule Revision during Rule Induction’,
*5th International Joint Conference on Artificial Intelligence,*pp. 305–310.Google Scholar - Mitchell, T.M. (1978),
*Version Spaces: An Approach to Concept Learning*. Technical Report STAN-CS-78–711, Computer Science Dept., Stanford University (Ph.D. dissertation).Google Scholar - Muggleton, 5. (1990),
*Inductive Acquisition of Expert Knowledge*Workingham, England: Addison-Wesley (Turing Institute Press).Google Scholar - Muggleton, S. (ed.) (1992),
*Inductive Logic Programming,*Academic Press.Google Scholar - Muggleton, S. and Feng, C. (1992), ‘Efficient Induction of Logic Programs’, in S Muggleton (ed.),
*Inductive Logic Programming,)*Academic Press. pp. 281–298.Google Scholar - Nutter, J.T. (1991), ‘Focus of Attention, Context, and the Frame Problem’, in in K. M. Ford and P. J. Hayes (eds.)
*Reasoning Agents in a Dynamic World*Greenwich, CT: JAI Press. pp. 171–188.Google Scholar - Oliver, J. Dowe, D. and Wallace, C. S. (1992), ‘Inferring Decision Graphs using the Minimum Message Length Principle’,
*Proceedings of the 1992 Australian Joint Conference on Artificial Intelligence, Hobart,*Tasmania, pp. 361–367, World Scientific.Google Scholar - Oliver, J. and Hand, D. (1995)
*Introduction to Minimum Encoding Inference*. Technical Report 95/205, Dept. of Computer Science, Monash University.Google Scholar - Pylyshyn, Z.W. (ed.) (1987),
*The Robot's Dilemma: The Frame Problem in Artificial Intelligence*Norwood, N.J.: Ablex.Google Scholar - Quinlan, J.R. (1990), ‘Learning Logical Definitions from Relations’,
*Machine Learning*5, pp 239–266.Google Scholar - Quinlan, J. R. (1993), C4.5:
*Programs for Machine Learning*, Morgan Kaufmann.Google Scholar - Reichenbach, H. (1949),
*The Theory of Probability,*second edition, translated by E. H. Hutton and M. Reichenbach, Berkeley: University of California.Google Scholar - Reiter, R. (1978), ‘On Reasoning by Default’,
*Proceedings of Theoretical Issues in Natural Language Processing,*Urbana, Illinois, pp. 210–218.Google Scholar - Reiter, R. (1980), ‘A Logic for Default Reasoning’,
*Artificial Intelligence*13, pp. 81–132.Google Scholar - Reiter, R., (1987), ‘Nonmonotonic Reasoning’,
*Annual Review of Computer Science*2, pp. 147–186.Google Scholar - Robinson, J.A. (1965), ‘A Machine-oriented Logic Based on the Resolution Principle’,
*Journal of the ACM*12, pp. 23–41.Google Scholar - Rumelhart, D. E. and McClelland, J. (1986),
*Parallel Distributed Processing, volume 1*, MIT.Google Scholar - Salmon, W.C. (1967),
*The Foundations of Scientific Inference*, University of Pittsburgh.Google Scholar - Salmon, W. C. (1971),
*Statistical Explanation and Statistical Relevance,*University of Pittsburgh.Google Scholar - Schaffer, C. (1994), ‘A Conservation Law for Generalization Performance’,
*Proceedings of the 1994 International Conference on Machine Learning*, Morgan Kaufmann.Google Scholar - Shannon, C. E. and Weaver, W. (1949)
*The Mathematical Theory of Communication,*University of Illinois.Google Scholar - Simon, H. (1994), ‘Literary Criticism: A Cognitive Approach’, in G. Guzeldere and S. Franchi (eds.),
*Stanford Humanities Review Special Supplement: Bridging the Gap*, pp. 1–26.Google Scholar - Solomonoff, R. (1964) ‘A Formal Theory of Inductive Inference, I and II,’
*Information and Control*7, 1–22 and 224–254.Google Scholar - Sperber, D. Premack, D. and Premack, A. J. (eds.), (1995),
*Causal Cognition: A Multidisciplinary Debate*, Oxford: Clarendon Press.Google Scholar - Spirtes, P., Glymour, C. and Schemes, R. (1993),
*Causation, Prediction, and Search*, New York: Springer Verlag.Google Scholar - Thorndike, E. L. (1911),
*Animal Intelligence*, New York: Macmillan.Google Scholar - Wallace, C. S. (1995)
*Multiple Factor Analysis by MML Estimation*. Technical Report 95/218, Dept. Computer Science, Monash University.Google Scholar - Wallace C. S. and Boulton D. M. (1968) ‘An Information Measure for Classification’,
*Computer Journal*11, 185–194.Google Scholar - Wallace, C. S. and Freeman, P. R. (1987), ‘Estimation and Inference by Compact Coding’,
*Journal of the Royal Statistical Society, Series B,*49, pp 240–252.Google Scholar - Wallace, C. S. and Freeman, P. R. (1992), ‘Single-factor analysis by minimum message length estimation’,
*Journal of the Royal Statistical Society, Series B,*54, pp. 195–209.Google Scholar - Wallace, C. S. and Korb, K. B. (forthcoming), ‘A Study of Causal Discovery by MML Sampling,’ forthcoming in M. Slater (ed.),
*Causal Models and Intelligent Data Analysis,*Springer Verlag.Google Scholar - Wallace, C. S. Korb, K. B. and Dai, H. (1996), ‘Causal Discovery via MML’, in L. Saitta (ed.),
*Proceedings of the 13th International Conference on Machine Learning,*Morgan Kaufmann, pp. 516–524.Google Scholar - Wolpert, D. H. and McReady, W. G. (1995),
*No Free Lunch Theorems for Search*. TR 95–02–010 Santa Fe Institute. http://www.santafe.edu/sfi/publications/95wplist.htmlGoogle Scholar - Wright, S. (1934) ‘The Method of Path Coefficients’,
*Annals of Mathematical Statistics*5, pp. 161–215.Google Scholar