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1 Université de Lyon 2 SILEX - Supporting Interaction and Learning by Experience LIRIS - Laboratoire d-InfoRmatique en Image et Systèmes d-information 3 IFSTTAR-LESCOT - Laboratoire d-Ergonomie et de Sciences Cognitives pour les Transports 4 PSU - Pennsylvania State University

Abstract : We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach.

Keywords : Sequence-mining Timeline-analysis Activity-trace Knowledge-Based-System Activity modeling





Autor: Olivier L. Georgeon - Alain Mille - Thierry Bellet - Benoît Mathern - Frank E. Ritter -

Fuente: https://hal.archives-ouvertes.fr/



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