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Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

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Solving a Problem With or Without a Program

Pei Wang
Published Online: 2013-01-04 | DOI: https://doi.org/10.2478/v10229-011-0021-5


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.

Keywords: problem; solution; program; computation; algorithm; self-programming; knowledge and resources restriction; case-by-case problem-solving

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

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

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

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

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

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

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

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

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

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

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

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

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

  • 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.Google Scholar

  • 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.Google Scholar

  • 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.Google Scholar

  • 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. Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • 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.Google Scholar

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

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

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

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

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

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

  • 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.Google Scholar

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

About the article

Published Online: 2013-01-04

Citation Information: Journal of Artificial General Intelligence, Volume 3, Issue 3, Pages 43–73, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/v10229-011-0021-5.

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