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Computing Commonsense

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BT Technology Journal

Abstract

How can we build systems with 'commonsense', the thinking skills that every ordinary person takes for granted? In this paper, we describe a multi-agent architecture for enabling commonsense reasoning which is in development at the Media Lab. The system reasons about the kinds of fundamental entities that show up in nearly all situations — such as people, objects, events, goals, plans and mistakes. The architecture supports multiple layers of reflective reasoning, mechanisms for coherent reasoning across multiple representations, and large-scale control structures called 'ways to think'. We first describe the main features of our architecture and then discuss its application and evaluation to an artificial life scenario.

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References

  1. McCarthy J: 'Programs with commonsense', in Mechanisation of Thought Processes', Proceedings of the Symposium of National Physics Laboratory, pp 77—84, London, HMSO (1959).

    Google Scholar 

  2. Singh P: 'The public acquisition of commonsense knowledge', in Proceedings of AAAI Spring Symposium: Acquiring (and Using) Linguistic (and World) Knowledge for Information Access, Palo Alto, CA, AAAI (2002).

  3. Lenat D: 'CYC: A large-scale investment in knowledge infrastructure', Communications of the ACM, 38, No 11, pp 33—38 (1995).

    Google Scholar 

  4. Singh P, Barry B and Liu H: 'Teaching machines about everyday life', BT Technol J, 22, No 4, pp 227—250 (October 2004).

    Google Scholar 

  5. Liu H and Singh P: 'ConceptNet — a practical commonsense reasoning tool-kit', BT Technol J, 22, No 4, pp 211—226 (October 2004).

    Google Scholar 

  6. Lieberman H, Faaborg A, Espinosa J and Stocky T: 'Commonsense on the go', BT Technol J, 22, No 4, pp 241—252 (October 2004).

    Google Scholar 

  7. Carbonell J: 'Derivational analogy: A theory of reconstructive problem solving and expertise acquisition', in Michalski R, Carbonell J and Mitchell T (Eds): 'Machine learning: an artificial intelligence approach', Morgan Kaufman Publishers, San Mateo, CA (1986).

    Google Scholar 

  8. Kuipers B: 'Qualitative simulation', Artificial Intelligence, 29, pp 289—338 (1986).

    Google Scholar 

  9. Minsky M: 'K-lines, a theory of memory', Cognitive Science, 4, pp 117—133 (1980).

    Google Scholar 

  10. Davis R: 'Diagnostic reasoning based on structure and behaviour', Artificial Intelligence, 24, pp 347—410 (1984).

    Google Scholar 

  11. Winston P H: 'Learning structural descriptions from examples', PhD thesis, Department of Electrical Engineering, MIT (1970).

    Google Scholar 

  12. Sussman G J: 'A computational model of skill acquisition', PhD thesis, Department of Mathematics, MIT (1973).

    Google Scholar 

  13. Pearl J: 'Probabilistic reasoning in intelligent systems', San Mateo, CA, Morgan Kaufmann (1988).

    Google Scholar 

  14. Amarel A: 'On representations of problems of reasoning about actions', in Michie D (Ed): 'Machine intelligence', 3, No 3, pp 131—171, Elsevier (1968).

  15. Minsky M: 'The Emotion Machine', (forthcoming).

  16. Minsky M: 'The society of mind', New York, Simon and Schuster (1986).

    Google Scholar 

  17. Polya G: 'How to solve it: a new aspect of mathematical method', Princeton, NJ, Princeton University Press (1957).

    Google Scholar 

  18. Singh P: 'A preliminary collection of reflective critics for layered agent architectures', Proceedings of the Safe Agents Workshop (AAMAS 2003), Melbourne, Australia (2003).

  19. Lakoff G and Johnson M: 'Metaphors We Live By', University of Chicago Press (1980).

  20. Singh P and Minsky M: 'An architecture for combining ways to think', Proceedings of the International Conference on Knowledge Intensive Multi-Agent Systems, Cambridge, MA (2003).

  21. Singh P and Minsky M: 'An architecture for cognitive diversity', in Davis D N (Ed): 'Visions of Mind', IDEA Group Inc (2004).

  22. McCarthy J, Minsky M, Sloman A, Gong L, Lau T, Morgenstern L, Mueller E, Riecken D, Singh M and Singh P: 'An architecture of diversity for commonsense reasoning', IBM Systems Journal, 41, No 3, pp 530—539 (2002).

    Google Scholar 

  23. Minsky M, Singh P and Sloman A: 'The St Thomas commonsense symposium: designing architectures for human-level intelligence', AI Magazine, 25, No 2, pp 113—124 (2004).

    Google Scholar 

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Singh, P., Minsky, M. & Eslick, I. Computing Commonsense. BT Technology Journal 22, 201–210 (2004). https://doi.org/10.1023/B:BTTJ.0000047599.89995.3c

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  • DOI: https://doi.org/10.1023/B:BTTJ.0000047599.89995.3c

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