Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments

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TypeArticle
ConferenceProceedings of the International Joint Conference on Artificial Intelligence 2005, July 30 - August 5, 2005., Edinburgh, Scotland
AbstractQualitative 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 quasimonotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm is introduced along with a scalebased definition of “flatness”.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48219
NPARC number8914266
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Record identifier9e1c123d-0756-42f9-9e7c-0cbee77f0ea6
Record created2009-04-22
Record modified2016-05-09
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