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Q2 Symbolic Reasoning about Noisy Dynamic Systems

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Abstract

Symbolic reasoning about continuous dynamic systems requires consistent qualitative abstraction functions and a consistent symbolic model. Classically, symbolic reasoning systems have utilized a box partition of the system space to achieve qualitative abstraction, but boxes can not provide a consistent abstraction. Our Q2 methodology abstracts a provably consistent symbolic representation of noise-free general dynamic systems. However, the Q2 symbolic representation has not been previously evaluated for efficacy in the presence of noise. We evaluate the effects of noise on Q2 symbolic reasoning in the domain of maneuver detection. We demonstrate how the Q2 methodology derives a symbolic abstraction of a general dynamic system model used in evaluating maneuver detectors. Simulation results represented by ROC curves show that the Q2 based maneuver detector is superior to a box-based detector. While no method is consistent in the presence of noise, the Q2 methodology is superior to the classic box's approach for deriving qualitative decisions about noisy dynamic systems.

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References

  1. Albus, J. S.: Outline for a theory of intelligence, IEEE Trans. Systems Man Cybernet. 21 (1991), 473–509.

    Google Scholar 

  2. Alur, R., Courcoubetis, C., Heniznger, T. A., and Ho, P.-H.: Hybrid automata: An algorithmic approach to the specification and verification of hybrid systems, in: R. L. Grossman, A. Nerode, A. P. Ravn, and H. Rischel (eds), Theory of Hybrid Systems, Lecture Notes in Comput. Sci. 736, Springer, Berlin, 1993, pp. 209–229.

    Google Scholar 

  3. Antsaklis, P. J. et al.: Towards intelligent autonomous control systems: Architecture and fundamental issues, Intelligent and Robotic Systems 1 (1989), 315–342.

    Google Scholar 

  4. Bar-Shalom, Y. and Fortmann, T. E.: Tracking and Data Association, Academic Press, New York, 1988.

    Google Scholar 

  5. Blackman, S. S.: Multiple-Target Tracking with Radar Applications, Artech House, 1986.

  6. Bolger, P. L.: Targeting a maneuvering target using input estimation, IEEE Trans. Aerospace Electronic Systems 23(3) (1987), 298–310.

    Google Scholar 

  7. Branicky, M. S., Borkar, V. S., and Mitter, S. K.: A unified framework for hybrid control:Model and optimal control theory, IEEE Trans. Automat. Control 43(1) (1998), 31–45.

    Google Scholar 

  8. Genesereth, M. R. and Nilsson, N. J.: Logical Foundations of Artificial Intelligence, Morgan Kaufman, Los Altos, CA, 1987.

    Google Scholar 

  9. Guckenheimer, J. and Johnson, S.: Planar hybrid systems, in: P. Antsaklis, W. Kohn, A. Nerode, and S. Sastry (eds), Hybrid Systems II, Lecture Notes in Comput. Sci. 999, Springer, Berlin, 1994.

    Google Scholar 

  10. Kokar, M. M.: Critical hypersurfaces and the quality space, in: 6th National Conf. on Artificial Intelligence, AAAI, 1987.

  11. Kokar, M. M.: Qualitative dynamics and fusion, in: 1st IEEE Conf. on Control Applications, Dayton, OH, 1992.

  12. Kokar, M. M.: On consistent symbolic representations of general dynamic systems, IEEE Trans. Systems Man Cybernet. 25(8) (1995), 1231–1242.

    Google Scholar 

  13. Korona, Z. and Kokar, M. M.: A fusion and learning algorithm for landing aircraft tracking: Compensating for exhaust plume disturbance, IEEE Trans. Aerospace Electronic Systems 31(3) (1995), 1210–1215.

    Google Scholar 

  14. Kuipers, B. J.: Qualitative simulation, Artificial Intelligence J. 29 (1986), 289–338.

    Google Scholar 

  15. Kuipers, B.: Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge, MIT Press, Cambridge, MA, 1994.

    Google Scholar 

  16. Lemmon, M., Stiver, J. A. et al.: Event identification and intelligent hybrid control, in: A. Nerode and W. Kohn (eds), Hybrid Systems, Lecture Notes in Comput. Sci. 736, Springer, Berlin, 1993.

    Google Scholar 

  17. Mesarovic, M. D. and Takahara, Y.: Abstract System Theory, Springer, Berlin, 1989.

    Google Scholar 

  18. Nerode, A. and Kohn, W.: Models for hybrid systems: Automata, topologies, controllability, observability, in: A. Nerode and W. Kohn (eds), Hybrid Systems, Lecture Notes in Comput. Sci. 736, Springer, Berlin, 1993.

    Google Scholar 

  19. Puri, A. and Varaiya, P.: Verification of hybrid systems using abstractions, in: P. Antsaklis, W. Kohn, A. Nerode, and S. Shastry (eds), Hybrid Systems II, Lecture Notes in Comput. Sci. 735, Springer, Berlin, 1995, pp. 359–369.

    Google Scholar 

  20. Strauss, P.: Problems of interval-based qualitative reasoning, in: D. S. Weld and J. deKleer (eds), Readings in Qualitative Reasoning about Physical Systems, Morgan Kaufman, Los Altos, CA, 1990, pp. 288–305.

    Google Scholar 

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Linder, S.P., Korona, Z. & Kokar, M.M. Q2 Symbolic Reasoning about Noisy Dynamic Systems. Journal of Intelligent and Robotic Systems 24, 295–311 (1999). https://doi.org/10.1023/A:1008071001301

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