Advertisement

A Decision-Theoretic Treatment of Imprecise Computation

  • John Yen
  • Swaminathan Natarajan
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 318)

Abstract

Imprecise computation has been suggested as a promising model of real-time computing in order to deal with timing constraints imposed by the environment. However, the theoretical foundation of the technique has not been fully explored. To address this, we propose a decision-theoretic foundation of imprecise computation. The main benefit of such a treatment is that it enables the qualitative assumptions underlying imprecise computation techniques to be explicitly stated in a formal way. The theoretical foundation laid out in this paper, hence, will not only enable the justification of using imprecise computation techniques for a real-time application, but will also facilitate the development of extended techniques for more complex real-time systems.

Keywords

Resource Allocation Resource Constraint Decision Theory Expected Utility Resource Requirement 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A.M. Agogino, R. Guha, and S. Russell. Sensor Fusion Using Influence Diagrams and Reasoning by Analogy: Application to Machine Monitoring and Control, pages 333–357. Computational Mechanics Publications, Southhampton, 1988.Google Scholar
  2. [2]
    J. S. Aikins. Prototypes and production rules: A knowledge representation for computer consultations. Technical Report STAN-CS-80-814, Department of Computer Science, Stanford University, 1980.Google Scholar
  3. [3]
    J. S. Breese and M. R. Fehling. Decision-theoretic control of problem solving: Principles and architecture. In Proceedings of Fourth Workshop on Uncertainty in Artificial Intelligence, pages 30–37, Minneapolis, MN, July 1988. AAAI.Google Scholar
  4. [4]
    P. R. Cohen, M. L. Greenberg, D. M. Hart, and A. E. Howe. Trial by fire: Understanding the design requirements for agents in complex environments. AI Magazine, 10(3):34–48, Fall 1989. This article describes the underlying methodology and illustrates the architecture and behavior of Phoenix.Google Scholar
  5. [5]
    P. R. Cohen, A. E. Howe, and D. M. Hart. Intelligent real-time problem solving: Issues and examples. In Intelligent Real-Time Problem Solving: Workshop Report. Cimflex Teknowledge Corp., January 1990.Google Scholar
  6. [6]
    Thomas Dean. An analysis of time-dependent planning. In Proceedings of AAAI-88, pages 49–54, 1988.Google Scholar
  7. [7]
    R. T. Dodhiawala, N. S. Sridharan, and C. Pickering. A Real-Time Blackboard Architecture, chapter 10. Academic Press, Boston, 1989.Google Scholar
  8. [8]
    R. T. Dodhiawala, N. S. Sridharan, P. Raulefs., and C. Pickering. Realtime ai systems: A definition and an architecture. In Proc. Inter. Joint Conf on Artificial Intelligence, pages 256–261, Detroit, MI, 1989.Google Scholar
  9. [9]
    J. Doyle. Artificial intelligence and rational self-government. Technical Report CS-88-124, Carnegie Mellon University, 1988.Google Scholar
  10. [10]
    M. R. Fehling and J. S. Breese. A computational model for the decision-theoretic control of problem solving under uncertainty. Technical Report Rockwell Technical Report 837-88-5, Rockwell International Science Center, April 1988.Google Scholar
  11. [11]
    D. Hansson and A. Mayer. The optimality of satisficing solutions. In Proceedings of Fourth Workshop on Uncertainty in Artificial Intelligence, pages 148–157, Minneapolis, MN, July 1988. AAAI.Google Scholar
  12. [12]
    B. Hayes-Roth. Intelligent monitoring and control. In Proc. Inter. Joint Conf. on Artificial Intelligence, pages 243–249, Detroit, MI, 1989.Google Scholar
  13. [13]
    B. Hayes-Roth. Architectural foundations for real-time performance in intelligent agents. Real-Time Systems, 2(1/2), May 1990.Google Scholar
  14. [14]
    D. E. Heckerman, J. S. Breese, and E. J. Horvitz. The compilation of decision models. In Proceedings of Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, Canada, August 1989. AAAI.Google Scholar
  15. [15]
    M. Hewett and B. Hayes-Roth. Real-time i/o in knowledge-based systems. In Proceedings of the Second Blackboard Workshop, pages 107–118, St. Paul, MN, August 1988. AAAI.Google Scholar
  16. [16]
    E. J. Horvitz. Rational metareasoning and compilation for optimizing decisions under bounded resources. In Proceedings of Computational Intelligence 89, Milan, September 1989. Association for Computing Machinery.Google Scholar
  17. [17]
    E. J. Horvitz, G. F. Cooper, and D. E. Heckerman. Reflection and action under scarce resources: Theoretical principles and empirical study. In Proc. Inter. Joint Conf. on Artificial Intelligence, pages 1121–1127, Detroit, MI, August 1989.Google Scholar
  18. [18]
    E.J. Horvitz. Reasoning about beliefs and actions under computational resource constraints. In Proceedings of Third Workshop on Uncertainty in Artificial Intelligence, Seattle, Washington, July 1987. AAAI.Google Scholar
  19. [19]
    E.J. Horvitz. Reasoning under varying and uncertain resource constraints. In Proc. National Conf. on Artificial Intelligence, pages 111–116, Minneapolis, MN, August 1988.Google Scholar
  20. [20]
    K. J. Lin, S. Natarajan, and J.W.S. Liu. Imprecise results: Utilizing partial computations in real-time systems. In Proceedings of the 8th Real-Time Systems Symposium, pages 210–217, San Jose, CA, 1987.Google Scholar
  21. [21]
    John McDermott. R1: A rule-based configurer of computer systems. Artificial Intelligence, 19(1):39–88, 1982.CrossRefGoogle Scholar
  22. [22]
    Swaminathan Natarajan. Building flexible real-time systems. PhD thesis, Univ. of Illinois, Urbana, Department of Computer Science, 1990.Google Scholar
  23. [23]
    W. J. Pardee and B. Hayes-Roth. Intelligent real-time control of material processing. Technical Report Technical Report #1, Rockwell International Science Center, 1987.Google Scholar
  24. [24]
    S. J. Russell. Execution architectures and compilation. In Proc. Inter. Joint Conf. on Artificial Intelligence, Detroit, Michigan, August 1989.Google Scholar
  25. [25]
    S. J. Russell and E. H. Wefald. Principles of metareasoning. In R. J. Brachman, H. J. Levesque, and R. Reiter, editors, Proceedings of the First Inter. Conf. on Principles of Knowledge Representation and Reasoning, Toronto, May 1989. Morgan Kaufman.Google Scholar
  26. [26]
    Edward H. Shortliffe. Computer-Based Medical Consultation: MYCIN. American Elsevier, 1976.Google Scholar
  27. [27]
    N. S. Sridharan and R. T. Dodhiawala. Real-time problem solving: Preliminary thoughts. In Intelligent Real-Time Problem Solving: Workshop Report. Cimflex Teknowledge Corp., January 1990.Google Scholar
  28. [28]
    R. Washington and B. Hayes-Roth. Input data management in real-time ai systems. In Proc. Inter. Joint Conf. on Artificial Intelligence, pages 250–255, Detroit, MI, 1989.Google Scholar
  29. [29]
    M. P. Wellman. Formulation of Tradeoffs in Planning Under Uncertainty. Pitman and Morgan Kaufmann, 1990.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • John Yen
    • 1
  • Swaminathan Natarajan
    • 2
  1. 1.Department of Computer ScienceTexas A&M UniversityCollege StationUSA
  2. 2.Xerox CorporationHenriettaUSA

Personalised recommendations