VIDAL Nicolas
责任导师 : Samir AKNINE
助理责任导师 : TAILLIBERT Patrick
Moving agents behaviours: a cognitive approach for intention recognition
In a maritime area supervision context, we seek providing a human operator with dynamic information on the behaviors of the monitored entities. Linking raw measurements, coming from sensors, with the abstract descriptions of those behaviors is a tough challenge. This problem is usually addressed with a two-stepped treatment: filtering the multidimensional, heterogeneous and imprecise measurements into symbolic events and then using efficient plan recognition techniques on those events. This allows, among other things, the possibility of describing high level symbolic plan steps without being overwhelmed by low level sensor specificities. However, the first step is information destructive and generates additional ambiguity in the recognition process. Furthermore, splitting the behavior recognition task leads to unnecessary computations and makes the building of the plan library tougher. Thus, we propose to tackle this problem without dividing the solution into two processes. We present a hierarchical model, inspired by the formal language theory, allowing us to describe behaviors in a continuous way, and build a bridge over the semantic gap between measurements and intents. Thanks to a set of algorithms using this model, we are able, from observations, to deduce the possible future developments of the monitored area while providing the appropriate explanations.
答辩 : 2014-11-28
评委会 :
René Mandiau, (LAMIH) [Rapporteur]
Philippe Mathieu, (LIFL) [Rapporteur]
Samir Aknine, (LIRIS)
Philippe Caillou, (LRI)
Amal El Fallah Segrouchni, examinateur (LIP6)
Patrick Taillibert (LIP6/Thales Systèmes Aéroportés)
2010-2014 刊物
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2014
- N. Vidal : “Comportements d’agents en mouvement : une approche cognitive pour la reconnaissance d’intentions”, 博士论文, 答辩 2014-11-28, 责任导师 Aknine, Samir, 助理责任导师 : Taillibert, Patrick (2014)
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2010
- N. Vidal, P. Taillibert, S. Aknine : “Online Behavior Recognition: A New Grammar Model Linking Measurements and Intents”, 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2010, vol. 2, Arras, France, pp. 129-137, (IEEE) (2010)