Adaptive decomposition of time
Jürgen Schmidhuber · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · 1991
: In this paper we introduce design principles for unsupervised detection of regularities (like causal relationships) in temporal sequences. One basic idea is to train an adaptive predictor module to predict future events from past events, and to train an additional confidence module to model the reliability of the predictor's predictions. We select system states at those points in time where there are changes in prediction reliability, and use them recursively as inputs for higher-level predictors. This can be beneficial for `adaptive sub-goal generation' as well as for `conventional' goal-directed (supervised and reinforcement) learning: Systems based on these design principles were successfully tested on tasks where conventional training algorithms for recurrent nets fail. Finally we describe the principles of the first neural sequence `chunker' which collapses a self-organizing multi-level predictor hierarchy into a single recurrent network. 1 OUTLINE OF THE PAPER This paper is ba...