METHODS AND TOOLS FOR MODELLING REASONING IN DIAGNOSTIC SYSTEMS

Alexander P. Eremeev, Vadim N. Vagin

Abstract

The methods of case-based reasoning for a solution of problems of real-time diagnostics and forecasting in intelligent decision support systems (IDSS) is considered. Special attention is drawn to a case library structure for real-time IDSS and an application of this reasoning type for diagnostics of complex object states. The problem of finding the best current measurement points in model-based device diagnostics with using Assumption-based Truth Maintenance Systems (ATMS) is viewed. The new heuristic approaches of current measurement point choosing on the basis of supporting and inconsistent environments are presented. This work was supported by the Russian Foundation for Basic Research (projects No 08-01-00437 and No 08-07-00212).

References

  1. Vagin V.N., Yeremeyev A.P., 2007. Modeling Human Reasoning in Intelligent Decision Support Systems // Proc. of the Ninth International Conference on Enterprise Information Systems. Volume AIDSS. Funchal, Madeira, Portugal, June 12-16, INSTICC, pp.277-282.
  2. Eremeev, A., Varshavsky, P., 2006. Analogous Reasoning and Case-Based Reasoning for Intelligent Decision Support Systems // International Journal INFORMATION Theories & Applications (ITHEA), vol. 13 ? 4, pp. 316-324.
  3. Aamodt, E., 1994. Plaza “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches” // AI Communications, No. 7.
  4. Eremeev, A., Varshavsky, P., 2007. Application of Casebased Reasoning for Intelligent Decision Support Systems // Proceedings of the XIIIth International Conference “Knowledge-Dialogue-Solution” - Varna, vol. 1, pp. 163-169.
  5. Eremeev, A., Varshavsky, P., 2008. Case-based Reasoning Method for Real-time Expert Diagnostics Systems // International Journal “Information Theories & Applications”, Volume 15, Number 2, pp. 119-125.
  6. Clancey W., 1985. Heuristic Classification. // Artificial Intelligence, 25(3), pp. 289-350.
  7. de Kleer, J. and Williams, B.C., 1987. Diagnosing Multiple Faults. // Artificial Intelligence, v.32, pp. 97- 130.
  8. Forbus K.D., de Kleer, J., 1993. Building Problem Solver. // A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England.
  9. de Kleer J., 1986. An Assumption-based TMS. // Artificial Intelligence, v.28, p.127-162.
  10. Vagin V.N., Golovina E.Ju., Zagoryanskaya A.A., Fomina M.V., 2008. Exact and Plausible Inference in Intelligent Systems. 2nd edition. // Vagin V.N., Pospelov D.A. (eds). M.: Fizmatlit, - 710 p. (in Russian).
  11. Frohlich P., 1998. DRUM-II Efficient Model-based Diagnosis of Technical Systems. //PhD thesis, University of Hannover.
Download


Paper Citation


in Harvard Style

P. Eremeev A. and N. Vagin V. (2009). METHODS AND TOOLS FOR MODELLING REASONING IN DIAGNOSTIC SYSTEMS . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-85-2, pages 271-276. DOI: 10.5220/0001832902710276


in Bibtex Style

@conference{iceis09,
author={Alexander P. Eremeev and Vadim N. Vagin},
title={METHODS AND TOOLS FOR MODELLING REASONING IN DIAGNOSTIC SYSTEMS},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2009},
pages={271-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001832902710276},
isbn={978-989-8111-85-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - METHODS AND TOOLS FOR MODELLING REASONING IN DIAGNOSTIC SYSTEMS
SN - 978-989-8111-85-2
AU - P. Eremeev A.
AU - N. Vagin V.
PY - 2009
SP - 271
EP - 276
DO - 10.5220/0001832902710276