Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 400-404.
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 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”.
@inproceedings{ijcai2005p102,
title = {Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments},
author = {Brooks, Martin and Yan, Yuhong and Lemire, Daniel},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {400--404},
year = {2005},
url = {https://ijcai.org/proceedings/2005/102},
}
TY - CPAPER TI - Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments AU - Brooks, Martin AU - Yan, Yuhong AU - Lemire, Daniel T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 400 EP - 404 UR - https://ijcai.org/proceedings/2005/102 ER -Copied
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