Solving a Problem With or Without a Program

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To solve a problem, an ordinary computer system executes an existing program. When no such program is available, an AGI system may still be able to solve a concrete problem instance. This paper introduces a new approach to do so in a reasoning system that adapts to its environment and works with insuffcient knowledge and resources. The related approaches are compared, and several conceptual issues are analyzed. It is concluded that an AGI system can solve a problem with or without a problem-specific program, and therefore can have human-like creativity and exibility.

Albus, J. S. 1991. Outline for a Theory of Intelligence. IEEE Transactions on Systems, Man, and Cybernetics 21(3):473–509.

Anderson, J. R. 1983. The Architecture of Cognition. Cambridge, Massachusetts: Harvard University Press.

Aristotle. 1882. The Organon, or, Logical treatises of Aristotle. London: George Bell. Translated by O. F. Owen.

Arkin, R. C. 1998. Behavior-Based Robotics. Cambridge, Massachusetts: MIT Press.

Baum, E. B. 2004. What is Thought? Cambridge, Massachusetts: MIT Press.

Boden, M. A. 1991. The Creative Mind. New York: BasicBooks.

Bratman, M. E.; Israel, D. J.; and Pollack, M. E. 1988. Plans and resource-bounded practical reasoning. Computational Intelligence 4(4):349–355.

Bringsjord, S., and Arkoudas, K. 2004. The modal argument for hypercomputing minds. Theoretical Computer Science 317:167–190.

Brooks, R. A. 1991. Intelligence without representation. Artificial Intelligence 47:139–159.

Cormen, T. H.; Leiserson, C. E.; Rivest, R. L.; and Stein, C. 2001. Introduction to Algorithms. MIT Press, McGraw-Hill Book Company, 2nd edition.

Davis, M. 1958. Computability and Unsolvability. New York: Mcgraw-Hill.

Dean, T., and Boddy, M. 1988. An analysis of time-dependent planning. In Proceedings of AAAI-88, 49–54.

Dreyfus, H. L. 1979. What Computers Can’t Do: Revised Edition. New York: Harper and Row.

Fikes, R. E., and Nilsson, N. J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3-4):189–208.

Flach, P. A., and Kakas, A. C. 2000. Abductive and inductive reasoning: background and issues. In Flach, P. A., and Kakas, A. C., eds., Abduction and Induction: Essays on their Relation and Integration. Dordrecht: Kluwer Academic Publishers. 1–27.

Franklin, S. 2007. A foundational architecture for artificial general intelligence. In Goertzel, B., and Wang, P., eds., Advance of Artificial General Intelligence. Amsterdam: IOS Press. 36–54.

Frege, G. 1999. Begriffsschrift, a formula language, modeled upon that of arithmetic, for pure thought. In van Heijenoort, J., ed., Frege and G¨odel: Two Fundamental Texts in Mathematical Logic. Lincoln, Nebraska: iUniverse. 1–82. Originally published in 1879.

Hayes, P. J. 1977. In defense of logic. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence, 559–565.

Hofstadter, D. R. 1979. G¨odel, Escher, Bach: an Eternal Golden Braid. New York: Basic Books.

Hopcroft, J. E., and Ullman, J. D. 1979. Introduction to Automata Theory, Language, and Computation. Reading, Massachusetts: Addison-Wesley.

Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Berlin: Springer.

Jeffrey, R. C. 1965. The Logic of Decision. New York: McGraw-Hill.

Kaelbling, L. P.; Littman, M. L.; and Moore, A. W. 1996. Reinforcement learning: a survey. Journal of Artificial Intelligence Research 4:237–285.

Kowalski, R. 1979. Logic for Problem Solving. New York: North Holland.

Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, Massachusetts: MIT Press.

Kugel, P. 1986. Thinking may be more than computing. Cognition 22:137–198.

Kurzweil, R. 2006. The Singularity Is Near: When Humans Transcend Biology. New York: Penguin Books.

Laird, J. E.; Newell, A.; and Rosenbloom, P. S. 1987. Soar: an architecture for general intelligence. Artificial Intelligence 33:1–64.

Littman, M. L.; Goldsmith, J.; and Mundhenk, M. 1998. The computational complexity of probabilistic planning. Journal of Artificial Intelligence Research 9:1–36.

Lloyd, J. W. 1987. Foundations of Logic Programming. New York: Springer-Verlag.

Lucas, J. R. 1961. Minds, machines and G¨odel. Philosophy XXXVI:112–127.

Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: W. H. Freeman & Co.

McCarthy, J., and Hayes, P. J. 1969. Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B., and Michie, D., eds., Machine Intelligence 4. Edinburgh: Edinburgh University Press. 463–502.

McCarthy, J. 1988. Mathematical logic in artificial intelligence. Dædalus 117(1):297–311.

Michalski, R. S. 1993. Inference theory of learning as a conceptual basis for multistrategy learning. Machine Learning 11:111–151.

Minsky, M. 1985. The Society of Mind. New York: Simon and Schuster.

Mitchell, T. M. 1997. Machine Learning. New York: McGraw-Hill.

Muggleton, S. 1991. Inductive logic programming. New Generation Computing 8(4):295–318.

Murphy, R. R. 2000. An Introduction to AI Robotics. Cambridge, Massachusetts: MIT Press.

Newell, A., and Simon, H. A. 1963. GPS, a program that simulates human thought. In Feigenbaum, E. A., and Feldman, J., eds., Computers and Thought. McGraw-Hill, New York. 279–293.

Newell, A. 1990. Unified Theories of Cognition. Cambridge, Massachusetts: Harvard University Press.

Nilsson, N. J. 1991. Logic and artificial intelligence. Artificial Intelligence 47:31–56.

Nivel, E., and Th´orisson, K. 2009. Self-Programming: Operationalizing Autonomy. In Proceedings of the Second Conference on Artificial General Intelligence, 150–155.

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. San Mateo, California: Morgan Kaufmann Publishers.

Peirce, C. S. 1931. Collected Papers of Charles Sanders Peirce, volume 2. Cambridge, Massachusetts: Harvard University Press.

Penrose, R. 1989. The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press.

Piaget, J. 1960. The Psychology of Intelligence. Paterson, New Jersey: Littlefield, Adams & Co.

Pollock, J. L. 2006. Against Optimality: The Logical Foundations of Decision-Theoretic Planning. Computational Intelligence 22(1):1–25.

Russell, S., and Norvig, P. 2010. Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey: Prentice Hall, 3rd edition.

Simon, H. A. 1957. Models of Man: Social and Rational. New York: John Wiley.

Solomonoff, R. J. 1964. A formal theory of inductive inference. Part I and II. Information and Control 7(1-2):1–22,224–254.

Sutton, R. S., and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, Massachusetts: MIT Press.

Thórisson, K. R., and Helgasson, H. P. 2012. Cognitive Architectures and Autonomy: A Comparative Review. Journal of Artificial General Intelligence 3(2):1–30.

Wang, P. 1995. Non-Axiomatic Reasoning System: Exploring the Essence of Intelligence. Ph.D. Dissertation, Indiana University.

Wang, P. 2000. The logic of learning. In Working Notes of the AAAI workshop on New Research Problems for Machine Learning, 37–40.

Wang, P. 2004a. The limitation of Bayesianism. Artificial Intelligence 158(1):97–106.

Wang, P. 2004b. Problem solving with insufficient resources. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 12(5):673–700.

Wang, P. 2005. Experience-grounded semantics: a theory for intelligent systems. Cognitive Systems Research 6(4):282–302.

Wang, P. 2006. Rigid Flexibility: The Logic of Intelligence. Dordrecht: Springer.

Wang, P. 2007. Three fundamental misconceptions of artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence 19(3):249–268.

Wang, P. 2008. What do you mean by ‘AI’. In Proceedings of the First Conference on Artificial General Intelligence, 362–373.

Wang, P. 2009a. Case-by-case problem solving. In Proceedings of the Second Conference on Artificial General Intelligence, 180–185.

Wang, P. 2009b. Formalization of Evidence: A Comparative Study. Journal of Artificial General Intelligence 1:25–53.

Wang, P. 2011. The Assumptions on Knowledge and Resources in Models of Rationality. International Journal of Machine Consciousness 3(1):193–218.

Wang, P. 2013. Non-Axiomatic Logic: A Model of Intelligent Reasoning. Singapore: World Scientific. (in press).

Xu, Y., and Wang, P. 2012. The frame problem, the relevance problem, and a package solution to both. Synthese.

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