Common Sense Reasoning – From Cyc to Intelligent Assistant

  • Kathy Panton
  • Cynthia Matuszek
  • Douglas Lenat
  • Dave Schneider
  • Michael Witbrock
  • Nick Siegel
  • Blake Shepard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3864)


Semi-formally represented knowledge, such as the use of standardized keywords, is a traditional and valuable mechanism for helping people to access information. Extending that mechanism to include formally represented knowledge (based on a shared ontology) presents a more effective way of sharing large bodies of knowledge between groups; reasoning systems that draw on that knowledge are the logical counterparts to tools that perform well on a single, rigidly defined task. The underlying philosophy of the Cyc Project is that software will never reach its full potential until it can react flexibly to a variety of challenges. Furthermore, systems should not only handle tasks automatically, but also actively anticipate the need to perform them. A system that rests on a large, general-purpose knowledge base can potentially manage tasks that require world knowledge, or “common sense” – the knowledge that every person assumes his neighbors also possess. Until that knowledge is fully represented and integrated, tools will continue to be, at best,idiots savants. Accordingly, this paper will in part present progress made in the overall Cyc Project during its twenty-year lifespan – its vision, its achievements thus far, and the work that remains to be done. We will also describe how these capabilities can be brought together into a useful ambient assistant application.

Ultimately, intelligent software assistants should dramatically reduce the time and cognitive effort spent on infrastructure tasks. Software assistants should be ambient systems – a user works within an environment in which agents are actively trying to classify the user’s activities, predict useful subtasks and expected future tasks, and, proactively, perform those tasks or at least the sub-tasks that can be performed automatically. This in turn requires a variety of necessary technologies (including script and plan recognition, abductive reasoning, integration of external knowledge sources, facilitating appropriate knowledge entry and hypothesis formation), which must be integrated into the Cyc reasoning system and Knowledge Base to be fully effective.


Natural Language Processing Inductive Logic Programming Abductive Reasoning Human Assistant Plan Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bollacker, K., Lawrence, S., Giles, C.L.: CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications. In: Proceedings of the 2nd International Conference on Autonomous Agents, New York, pp. 116–123 (1998)Google Scholar
  2. 2.
    Brin, S., Page, L.: Anatomy of a large-scale hypertextual search engine. In: Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, pp. 107–117 (1998)Google Scholar
  3. 3.
    Buchanan, B.G., Feigenbaum, E.A.: DENDRAL and Meta-DENDRAL: Their applications dimension. Journal of Artificial Intelligence 11, 5–24 (1978)CrossRefGoogle Scholar
  4. 4.
    Burns, K., Davis, A.: Building and maintaining a semantically adequate lexicon using Cyc. In: Viegas, E. (ed.) Breadth and Depth of Semantic Lexicons. Kluwer, Dordrecht (1990)Google Scholar
  5. 5.
    Copeland, B.J.: Artificial intelligence, Encyclopædia Britannica (2005), Online:
  6. 6.
    Day, D., Aberdeen, J., Hirschman, L., Kozierok, R., Robinson, P., Vilain, M.: Mixed initiative development of language processing systems. In: Proceedings of the Fifth Conference on Applied Natural Language Processing, Washington, D.C., pp. 348–355 (1997)Google Scholar
  7. 7.
    Hobbs, J., Appelt, D., Bear, J., Israel, D., Kameyama, M., Stickel, M., Tyson, M.: FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In: Roche, E., Schabes, Y. (eds.) Finite State Devices for Natural Language Processing, pp. 383–406. MIT Press, Cambridge (1997)Google Scholar
  8. 8.
    Klein, D., Smarr, J., Nguyen, H., Manning, C.: Named entity recognition with character-level models. In: Proceeedings of the Seventh Conference on Natural Language Learning, pp. 180–183 (2003)Google Scholar
  9. 9.
    Lenat, D.B., Borning, A., McDonald, D., Taylor, C., Weyer, S.: Knoesphere: building expert systems with encyclopedic knowledge. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 1, pp. 167–169 (1983)Google Scholar
  10. 10.
    Lenat, D.B., Guha, R.V.: Building Large Knowledge Based Systems. Addison-Wesley, Reading (1990)Google Scholar
  11. 11.
    Masters, J., Güngördü, Z.: Structured knowledge source integration: A progress report. Integration of Knowledge Intensive Multiagent Systems, Cambridge, Massachusetts (2003)Google Scholar
  12. 12.
    Matuszek, C., Witbrock, M., Kahlert, R.C., Cabral, J., Schneider, D., Shah, P., Lenat, D.: Searching for common sense: Populating Cyc™ from the Web. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh, PA (in Press, 2005)Google Scholar
  13. 13.
    Mayer, M.C., Pirri, F.: Abduction is not deduction-in-reverse. Journal of the IGPL 4(1), 1–14 (1996)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    McCallum, A., Nigam, K., Rennie, J., Seymore, K.: A machine learning approach to building domain-specific search engines. In: Proceedings of the 16th National Conference on Artificial Intelligence (AAAI 1999), pp. 662–667 (1999)Google Scholar
  15. 15.
    Prager, J., Brown, E., Coden, A., Radev, D.: Question answering by predictive annotation. In: Proceedings of the 23rd SIGIR Conference, pp. 184–191 (2000)Google Scholar
  16. 16.
    Quinlan, R.J., Cameron-Jones, R.M.: FOIL: A midterm report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3–20. Springer, Heidelberg (1993)Google Scholar
  17. 17.
    Schneider, D., Matuszek, C., Shah, P., Kahlert, R.C., Baxter, D., Cabral, J., Witbrock, M., Lenat, D.: Gathering and managing facts for intelligence analysis. In: Proceedings of the 2005 Conference on Intelligence Analysis: Methods and Tools, Lean, VA (2005)Google Scholar
  18. 18.
    Shortliffe, E.: Computer-based Medical Consultations: MYCIN. American Elsevier, New York (1976)Google Scholar
  19. 19.
    Siegel, N., Shepard, C.B., Cabral, J., Witbrock, M.J.: Hypothesis generation and evidence assembly for intelligence analysis: Cycorp’s Noöscape application. In: Proceedings of the 2005 Conference on Intelligence Analysis: Methods and Tools, McLean, VA (2005)Google Scholar
  20. 20.
    Sleator, D.D., Temperley, D.: Parsing English with a Link Grammar. Technical Report CMU-CS-91-196, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA (1991)Google Scholar
  21. 21.
    Srinivasan, A.: The Aleph Manual. University of Oxford,
  22. 22.
    Srinivasan, A., King, R.D., Bain, M.E.: An empirical study of the use of relevance information in inductive logic programming. Journal of Machine Learning Research 4(7), 369–383 (2003)MathSciNetMATHGoogle Scholar
  23. 23.
    Witbrock, M., Matuszek, C., Brusseau, A., Kahlert, R.C., Fraser, C.B., Lenat, D.: Knowledge begets knowledge: Steps towards assisted knowledge acquisition in Cyc. In: Proceedings of the AAAI 2005 Spring Symposium on Knowledge Collection from Volunteer Contributors (KCVC), Stanford, CA (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kathy Panton
    • 1
  • Cynthia Matuszek
    • 1
  • Douglas Lenat
    • 1
  • Dave Schneider
    • 1
  • Michael Witbrock
    • 1
  • Nick Siegel
    • 1
  • Blake Shepard
    • 1
  1. 1.Cycorp, Inc.AustinUSA

Personalised recommendations