Article thumbnail
Location of Repository

Monotonicity Analysis for Constructing Qualitative Models

By Yuhong Yan, Daniel Lemire and Martin Brooks


Qualitative models are more suitable than classical quantitative models in many tasks like Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotone pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper introduces an approach based on scale-dependent monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. the sensor data the simulation results, can be partitioned into quasi-monotone segments, i.e. segments monotone with respect to nonzero scale. We can identify the extrema of the quasi-monotone segments. This paper then uses this method to abstract qualitative models from simulation models for the purpose of diagnosis. It shows that using monotone analysis, the abstracted qualitative model is not only sound, but also parsimonious because it generates few landmarks

Topics: Artificial Intelligence
Year: 2004
OAI identifier:

Suggested articles


  1. (2003). A toolbox integrating model-based diagnosability analysis and automated generation of diagnostics.
  2. (2001). An online algorithm for segmenting time series.
  3. (1994). Approximation complexity for piecewise monotone functions and real data.
  4. (2002). Automated abstraction of numerical simulation models - theory and practical experience.
  5. (2003). Deriving qualitative deviations from matlab models.
  6. (2001). Induction of qualitative tree.
  7. (1992). Interaction-based invention: designing devices from first principles.
  8. (2003). Qualitative model abstraction for diagnosis.
  9. (1984). Qualitative process theory.
  10. (1986). Qualitative simulation.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.