QUALITATIVE AND QUANTITATIVE PROBABILISTIC TEMPORAL REASONING - for Industrial Applications

Gustavo Arroyo Figueroa

2004

Abstract

Many real-world domains, such as industrial diagnosis, require an adequate representation that combines uncertainty and time. Research in this field involves the development of new knowledge representation and inference mechanisms to deal with uncertainty and time. Current temporal probabilistic models become too complex when used for real world applications. In this paper, we propose a model, Temporal Events Bayesian Networks (TEBN), based on a natural extension of a simple Bayesian network. TEBN tries to make a balance between expressiveness and computational efficiency. Based on a temporal node definition, causal-temporal dependencies are represented by qualitative and quantitative relations, using different time intervals within each variable (multiple granularity). Qualitative knowledge about temporal relations between variables is used to facilitate the acquisition of the quantitative parameters. The inference mechanism combines qualitative and quantitative reasoning. The proposed approach is applied to a thermal power plant through a detailed case study, with promising results.

References

  1. J. F. Allen, 1983. Maintaining Knowledge about Temporal Intervals. Communications of the ACM, 26(11):832- 843.
  2. G. Arroyo-Figueroa and L.E. Sucar, 1999. A temporal Bayesian Network for diagnosis and prediction, In Proc. 15th UAI Conference, 13-20.
  3. G. Arroyo-Figueroa, Y. Alvarez and L.E. Sucar, 2000, SEDRET-an intelligent system for the diagnosis and prediction of events in power plants, Expert Systems with Applications, 18:75-86.
  4. S. F. Galan and F. J. Diez, 2002. Networks of probabilistic events in discrete time, Int. J. of Approximate Reasoning, 30 : 181-202.
  5. Peter Haddawy, 1996. A Logic of Time, chance, and action for representing plans. Artificial Intelligence, 80(2), 243-308, 1996.
  6. K. Murphy, 2002, Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, UC Berkeley, Computer Science Division.
  7. Judea Pearl, 2000, Causality, models, reasoning and inference, Cambridge University Press: London.
  8. E. Santos Jr. and J. D. Young, 1996, Probabilistic Temporal Networks. Report AFIT/EN/TR96-006 AFIT.
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Paper Citation


in Harvard Style

Figueroa G. (2004). QUALITATIVE AND QUANTITATIVE PROBABILISTIC TEMPORAL REASONING - for Industrial Applications . In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-12-0, pages 151-156. DOI: 10.5220/0001133701510156


in Bibtex Style

@conference{icinco04,
author={Gustavo Arroyo Figueroa},
title={QUALITATIVE AND QUANTITATIVE PROBABILISTIC TEMPORAL REASONING - for Industrial Applications},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2004},
pages={151-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001133701510156},
isbn={972-8865-12-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - QUALITATIVE AND QUANTITATIVE PROBABILISTIC TEMPORAL REASONING - for Industrial Applications
SN - 972-8865-12-0
AU - Figueroa G.
PY - 2004
SP - 151
EP - 156
DO - 10.5220/0001133701510156