Handling Time-Warped Sequences with Neural Networks

Being able to deal with time-warped sequences is crucial for a large number of tasks autonomous agents can be faced with in real-world environments, where robustness concerning natural temporal variability is required, and similar sequences of events should automatically be treated in asimilar way. Such tasks can easily be dealt with by natural animals, but equipping an animat with this capability is rather difficult. The presented experiments show how this problem can be solved with a neural network by ensuring slow state changes. An animat equipped with such a network not only adapts to the environment by learning from a number of examples, but also generalizes to yet unseen time-warped sequences.