IMPLEMENTATION OF USER-ADAPTIVE ASSISTANTS WITH NEURAL OPERATOR MODELS

In this paper the concept of user-adaptive assistants is discussed in the context of user-centered automation. It is shown that user behavio r, which is needed to control an assistant adaptively, can be identifi ed online from observed operator activities at the interface. The appl icability of neural networks for the implementation of operator models is studied. A two-lane car driving task is used as an experimental pa radigm for this analysis. Various network architectures are tested. Th is includes a combination of functional link and backpropagation as a novel, rapidly trainable structure. It is shown experimentally, that i ndividual human driving characteristics are indeed identifiable from t he input/output relations of a trained networks. The applicability of such models to an adaptive driver assistant is demonstrated.