title: Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments
creator: Brooks, Martin
creator: Yan, Yuhong
creator: Lemire, Daniel
subject: Artificial Intelligence
description: 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".
date: 2005
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4495/1/ijcai05_web.pdf
identifier:   Brooks, Martin and Yan, Yuhong and Lemire, Daniel  (2005) Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments.  [Conference Paper]     
relation: http://cogprints.org/4495/