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Brooks, Martin; Yan, Yuhong, & Lemire, Daniel (2005). Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Edinburgh, UK : IJICAI.
File(s) available for this item:| PDF - ijcai05_web.pdf |
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| Item Type: | Papers in Conference Proceedings |
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| Refereed: | Yes |
| Status: | Published |
| 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". |
| Official URL: | http://www.ijcai.org/papers/0890.pdf |
| Depositor: | Lemire, Daniel |
| Owner / Manager: | Daniel Lemire |
| Deposited: | 05 Jun 2007 |
| Last Modified: | 16 Jul 2015 00:47 |
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