Adaptive pattern recognition

A long term goal of research in artificial intelligence is to determine and to implement principles which permit a movable machine to direct its actions depending upon sensory feed-back from its environment. This paper concentrates on spatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adapts to the actual data, and examples are given of input prediction, of detection of unexpected events, and of recognition of spatial patterns. The image sequence is locally partitioned into temporally contiguous subsequences of a fixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximized subject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate the versatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different pattern recognition tasks.