Causal understanding in reasoning about the world

  • B. Chandrasekaran
Invited Talks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 869)


In this paper I survey over a decade of work on how we understand how things work. Much of this work has been conducted in the context of reasoning about functions of devices. I briefly overview a representational framework called Functional Representation, and indicate how a device representation in this language can be used for simulation, diagnosis and design. I make remarks about the generality of this approach in terms of causal understanding.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allemang, D.: Using functional models in automatic debugging. IEEE Expert. 6 (1991), 13–18Google Scholar
  2. 2.
    Chandrasekaran, B., Smith, J. W. Jr., Sticklen, J.: 'Deep’ Models and their relation to diagnosis. Artificial Intelligence in Medicine 1 (1989) 29–40Google Scholar
  3. 3.
    Chandrasekaran, B., Goel, A., Iwasaki, Y.: Functional representation as design rationale. IEEE Computer, Special Issue on Concurrent Engineering (1993) 48–56Google Scholar
  4. 4.
    Chandrasekaran, B.: Functional representation and causal processes. In Advances in Computers 38 (1994) 73–143. Academic PressGoogle Scholar
  5. 5.
    Chittaro, L., Tasso, C., and Toppano, E., Putting Functional Knowledge on Firmer Ground. In Reasoning About Function, Amruth N. Kumar, ed., American Association for Artificial Intelligence Workshop Program (1993) 23–30Google Scholar
  6. 6.
    de Kleer, J. Brown, J.S.: A qualitative physics based on confluences. Artificial Intelligence, 24 (1984) 7–83Google Scholar
  7. 7.
    Forbus, K.: Qualitative process theory. Artificial Intelligence 24 (1984) 85–168Google Scholar
  8. 8.
    Forbus, K.: Qualitative physics: Past, present and future. Exploring Artificial Intelligence, H. Shrobe, ed., San Mateo, CA: Morgan Kauffman (1988) 239–296Google Scholar
  9. 9.
    Goel, A.K.: Integration of Case-Based Reasoning and Model-Based Reasoning for Adaptive Design Problem Solving. Ph. D. thesis, The Ohio State University, LAIR (1989)Google Scholar
  10. 10.
    Goel, A.K.: A model-based approach to case adaptation. In Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, Chicago, August 7–10 (1991) 143–148, Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
  11. 11.
    Hodges, J.: Naive mechanics: a computational model of device use and function in design improvisation. IEEE Expert 7 (1992) 14–27Google Scholar
  12. 12.
    Iwasaki, Y., Chandrasekaran, B.: Design verification through function-and behavior-oriented representations: Bridging the gap between function and behavior. Artificial Intelligence in Design '92, John S. Gero, ed., Kluwer Academic Publishers (1992) 597–616Google Scholar
  13. 13.
    Iwasaki, Y., Fikes, R., Vescovi, M., Chandrasekaran, B.: How things are intended to work: Capturing functional knowledge in device design. In Proceedings of the 13th International Join Conference of Artificial Intelligence, San Mateo, CA: Morgan Kaufmann (1993) 1516–1522Google Scholar
  14. 14.
    Josephson, J. R.: The Functional Representation Language FR as a Family of Data Types, The Ohio State University, Laboratory for Artificial Intelligence Research, Columbus, OH, tech report (1993)Google Scholar
  15. 15.
    Keuneke, A., Allemang, D.: Understanding devices: Representing dynamic states. Technical Report, The Ohio State University, Laboratory for Artificial Intelligence Research, Columbus, OH (1988)Google Scholar
  16. 16.
    Keuneke, A.: Machine Understanding of Devices: Causal Explanation of Diagnostic Conclusions. Ph. D thesis. The Ohio State University (1989)Google Scholar
  17. 17.
    Pegah, M., Sticklen, J., Bond, W.: Functional representation and reasoning about the F/A-18 aircraft fuel system. IEEE Expert 8 (1993) 65–71Google Scholar
  18. 18.
    Prabhakar, S. and Goel, A. K.: Integrating case-based and model-based reasoning for creative design: constraint discovery, model revision and case composition. In Proceedings of the Second International Conference on Computational Models of Creative Design, Heron Island, Australia, (1992) Kluwer Academic PressGoogle Scholar
  19. 19.
    Sembugamoorthy, V., and Chandrasekaran, B.: Functional representation of devices and compilation of diagnostic problem-solving systems. In Experience, Memory, and Learning, J. Kolodner and C. Reisbeck, editors, Lawrence Erlbaum Associates (1986) 47–73Google Scholar
  20. 20.
    Sticklen, J.H.: MDX2, an integrated medical diagnostic system, Ph. D. dissertation, The Ohio State University, Columbus, OH (1987)Google Scholar
  21. 21.
    Sticklen, J., Kamel, A., & Bond, W. E. (1991): Integrating quantitative and qualitative computations in a functional framework. Engineering Applications of Artificial Intelligence 4(1) (1991) 1–10.Google Scholar
  22. 22.
    Sticklen, J., & Tufankji, R.: Utilizing a functional approach for modeling biological systems. Mathematical and Computer Modeling 16 (1992) 145–160Google Scholar
  23. 23.
    Stroulia, E., Shankar, M., Goel, A.K., and Penberthy, L.: A model-based approach to blame assignment in design. In Proceedings of the Second International Conference on AI in Design., Kluwer Academic Press (1992) 519–538Google Scholar
  24. 24.
    Stroulia, E., and Goel, A.K.: Generic teleological mechanisms and their use in case adaptation. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (1992) 319–324Google Scholar
  25. 25.
    Sun, J., and Sticklen, J.: Steps toward tractable envisionment via a functional approach. In The Second AAAI Workshop on Model Based Reasoning, American Association for Artificial Intelligence, Boston (1990) 50–55Google Scholar
  26. 26.
    Thadani, S.: Constructing Functional Models of a Device from Its Structural Description. Ph. D Thesis, Department of Computer & Information Science, The Ohio State University (1994)Google Scholar
  27. 27.
    Toth, S.: Using Functional Representation for Smart Simulation of Devices, Ph. D Thesis, Department of Computer & information Science, The Ohio State University (1993)Google Scholar
  28. 28.
    Vescovi, M., Iwasaki, Y., Fikes, R., and Chandrasekaran, B.: CFRL: A language for specifying the causal functionality of engineered devices. In Proceedings of the Eleventh National Conference on AI, American Association for Artificial Intelligence (1993) 626–633. AAAI Press/MIT PressGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • B. Chandrasekaran
    • 1
  1. 1.Laboratory for AI ResearchThe Ohio State UniversityColumbusUSA

Personalised recommendations