Skip to main content
Article thumbnail
Location of Repository

Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments

By Martin Brooks, Yuhong Yan and Daniel Lemire

Abstract

Qualitative models are often more suitable than classical quantitative models in tasks such as 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 monotonic pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper presents scale-based monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. sensor data or simulation results, can be partitioned into quasi-monotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm is introduced along with a scale-based definition of "flatness"

Topics: Artificial Intelligence
Year: 2005
OAI identifier: oai:cogprints.org:4495

Suggested articles

Citations

  1. (2001). An online algorithm for segmenting time series.
  2. (1994). Approximation complexity for piecewise monotone functions and real data.
  3. (2002). Automated abstraction of numerical simulation models - theory and practical experience.
  4. (2003). Deriving qualitative deviations from matlab models.
  5. (1974). Isotone optimization I.
  6. (1999). Knowledge-based event detection in complex time series data. In
  7. (1984). Qualitative process theory.
  8. (1994). Qualitative Reasoning.
  9. (1986). Qualitative simulation.
  10. (2000). Semi-quantitative system identification.

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