ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Action selection and task sequence learning for hybrid dynamical cognitive agents
Robotics and Autonomous Systems
Modeling timing structure in multimedia signals
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Multi-mode saliency dynamics model for analyzing gaze and attention
Proceedings of the Symposium on Eye Tracking Research and Applications
Estimation algorithm of machine operational intention by bayes filtering with self-organizing map
Advances in Human-Computer Interaction
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This paper addresses the parameter estimation problem of an interval-based hybrid dynamical system (interval system). The interval system has a two-layer architecture that comprises a finite state automaton and multiple linear dynamical systems. The automaton controls the activation timing of the dynamical systems based on a stochastic transition model between intervals. Thus, the interval system can generate and analyze complex multivariate sequences that consist of temporal regimes of dynamic primitives. Although the interval system is a powerful model to represent human behaviors such as gestures and facial expressions, the learning process has a paradoxical nature: temporal segmentation of primitives and identification of constituent dynamical systems need to be solved simultaneously. To overcome this problem, we propose a multiphase parameter estimation method that consists of a bottom-up clustering phase of linear dynamical systems and a refinement phase of all the system parameters. Experimental results show the method can organize hidden dynamical systems behind the training data and refine the system parameters successfully.