Advertisement

Applied Intelligence

, Volume 8, Issue 3, pp 235–245 | Cite as

An Intelligent Man-Machine Dialogue System Based on AI Planning

  • Bart Kuijpers
  • Kris Dockx
Article
  • 118 Downloads

Abstract

We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate its dialogue actions. A plan library allows it to follow loosely predetermined dialogue scenarios.

The heart of the coaching system is an AI planning module, which plans a series of dialogue actions. We present a coherent set of three dialogue or speech actions that will make up the physical form of the man-machine communication.The use of the AI planning paradigm as a basis for man-machine interaction is motivated by research in various disciplines, as e.g., AI, Cognitive Science and Social Sciences. Starting from the man-man communication metaphor, we can view the “thinking before speaking” of a human communication partner as constructing an underlying plan which is responsible for the purposiveness, the organisation and the relevance of the communication.

CALLIOPE has been fully implemented and tested on theoretical examples. At present, also three tailored versions of CALLIOPE are in operational use in different industrial application domains: operator support for remedying tasks in chemical process industry, operator support for a combined task of planning, plan execution and process control in the area of chemical process development, and thirdly decision support in production scheduling.

man-machine interaction dialogue planning AI planning scenarios 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.F. Allen and C.R. Perrault, “Analysing intentions in utterances,” Artificial Intelligence, vol. 15, no.3, pp. 143-178, 1980.Google Scholar
  2. 2.
    J.A. Ambros-Ingerson and S. Steel, “Integrated planning, execution, and monitoring,” in Proceedings of AAAI 1988, 1988, pp. 65-69.Google Scholar
  3. 3.
    D.E. Appelt, “Planning English referring expressions,” Artificial Intelligence, vol. 26, pp. 1-33, 1985.Google Scholar
  4. 4.
    D. Chapman, “Planning for conjunctive goals,” Artificial Intelligence, vol. 32, pp. 333-377, 1987.Google Scholar
  5. 5.
    P.R. Cohen and C.R. Perrault, “Elements of a plan-based theory of speech acts,” Cognitive Science, vol. 3, pp. 177-212, 1979.Google Scholar
  6. 6.
    K. Dockx and R. Timmermans, “Adding a temporal dimension to expert systems working in a real-time environment,” Communication and Cognition-Artificial Intelligence, CCAI, vol. 9, nos.2-3, pp. 253-257, 1992.Google Scholar
  7. 7.
    K. Dockx and K. Meert, “Production scheduling in a chemical process environment based on a simulated annealing algorithm,” Journal A, vol. 34, pp. 41-46, 1993.Google Scholar
  8. 8.
    K. Dockx, K. Meert, and B. Kuijpers, “Production scheduling in a chemical environment based on simulated annealing,” in Proceedings of AAAI SIGMAN Workshop, New Orleans, 1994.Google Scholar
  9. 9.
    R.E. Fikes and N. Nilsson, “STRIPS: A new approach to the application of theorem proving to problem solving,” Artificial Intelligence, vol. 5, no.2, pp. 189-208, 1971.Google Scholar
  10. 10.
    E. Fink and Q. Yang, “Automatically abstracting effects of operators,” in Proceedings of the First International Conference on AI Planning Systems, 1992, pp. 243-251.Google Scholar
  11. 11.
    E. Fink and Q. Yang, “Characterizing and automatically finding primary effects in planning,” IJCAI-93, pp. 1374-1379, 1993.Google Scholar
  12. 12.
    B.J. Grosz and C.L. Sidner, “Plans for discourse,” in Intentions in Communication, edited by P.R. Cohen, J. Morgan, and M.E. Pollack, MIT Press: Cambridge, MA, pp. 417-444, 1990.Google Scholar
  13. 13.
    R.W. Hill, Jr. and W.L. Johnson, “Situated plan attribution for intelligent tutoring,” in Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-94, 1994, pp. 499-505.Google Scholar
  14. 14.
    E. Hollnagel and D.D. Woods, “Cognitive systems engineering: New wine in new bottles,” International Journal of Man-Machine Studies, vol. 18, pp. 583-600, 1983.Google Scholar
  15. 15.
    S. Kambhampati, “On the utility of systematicity: Understanding tradeoffs between redundancy and commitment in partial-order planning,” IJCAI-93, pp. 1380-1385, 1993.Google Scholar
  16. 16.
    K. Kanev and K. Dockx, “A framework for graphically-oriented human computer interactions in intelligent operator support systems,” Comput. and Graphics, vol. 18, no.4, pp. 563-570, 1994.Google Scholar
  17. 17.
    J.D. Moore, Participating in Explanatory Dialogues. Interpreting and Responding to Questions in Context, MIT Press: Cambridge, 1995.Google Scholar
  18. 18.
    D.J. Musliner, “Using abstraction and nondeterminism to plan reaction loops,” in Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-94, 1994, pp. 1036-1041.Google Scholar
  19. 19.
    S.H. Nwana, “Intelligent tutoring systems: An overview,” Artificial Intelligence Review, vol. 4, pp. 251-277, 1990.Google Scholar
  20. 20.
    L. Quinn and D.M. Russel, “Intelligent interfaces: User models and planners,” in Proceedings CHI' 86, April 1986, pp. 314-320.Google Scholar
  21. 21.
    J. Rasmussen, “Cognitive systems analysis for risk management,” in Proceedings of SITEF 91-International Symposium on Cognitive Interactions, 1991, pp. 24-25.Google Scholar
  22. 22.
    E. Sacerdoti, “Planning in a hierarchy of abstraction spaces,” Artificial Intelligence, vol. 5, no.2, pp. 115-135, 1975.Google Scholar
  23. 23.
    S. Steel, “The bread and butter of planning,” Artificial Intelligence Review, vol. 1, pp. 159-181, 1987.Google Scholar
  24. 24.
    L.A. Suchman, Plans and Situated Actions. The Problem of Human Machine Communication, Cambridge University Press, 1987.Google Scholar
  25. 25.
    A. Tate, J. Hendler, and M. Drummond, “A review of AI planning techniques,” in Readings in Planning, edited by J. Allen, J. Hendler, and A. Tate, Morgan Kaufmann Publishers: California, pp. 26-49, 1990.Google Scholar
  26. 26.
    P. Van Bael, “Scheduling chemical batch processes with transfer times and sequence dependent set-up times in ZW or NIS policy,” The Second International Conference on the Practical Applications of Constraint Technology, London, UK, 1996, pp. 453-464.Google Scholar
  27. 27.
    R. Wilensky, D.N. Chin, M. Luria, J. Martin, J. Mayfield, and D. Wu, “The Berkeley UNIX consultant project,” Computational Linguistics, vol. 14, no.4, pp. 35-84, 1988.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Bart Kuijpers
    • 1
  • Kris Dockx
    • 2
  1. 1.Department of Mathematics and Computer ScienceUniversity of Antwerp (UIA)Antwerpen
  2. 2.Department of Chemical EngineeringKatholieke Universiteit Leuven

Personalised recommendations