Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments

Brooks, Martin; Yan, Yuhong et Lemire, Daniel (2005). « Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments », dans IJCAI05 (International Joint Conference on Artificial Intelligence, Edinburgh, UK, July 30-August 5, 2005)

Fichier(s) associé(s) à ce document :
[img]
Prévisualisation
PDF
Télécharger (179kB)

Résumé

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".

Type: Communication, article de congrès ou colloque
Mots-clés ou Sujets: Piecewise Quasi-Monotone Functions, Model-Based Diagnostic, Qualitative Model Abstraction
Unité d'appartenance: Télé-université > UER Science et Technologie
Déposé par: Daniel Lemire
Date de dépôt: 05 juin 2007
Dernière modification: 01 nov. 2014 02:03
Adresse URL : http://www.archipel.uqam.ca/id/eprint/316

Statistiques

Voir les statistiques sur cinq ans...