Abstract: A probabilistic learning element for performing task independent sequential pattern recognition. The element receives sequences of objects and outputs sequences of recognized states composed of objects. A plurality of memory elements are utilized to store received objects in sequence and for storing in context learned information including previously learned states, objects contained in previously learned states, positional information for each object in a learned state and other predetermined types of knowledge relating to previously learned states and objects contained therein. The element correlates sequences of received objects with learned information relating to previously learned states for providing conditional probabilities to possible sequences of recognized states. The most likely state sequence is determined and outputted as a recognized sequence when the element detects that a state has ended.

Abstract: A probabilistic learning element for performing task independent sequential pattern recognition employs context driven searching. The element receives sequences of objects and outputs sequences of recognized states composed of objects. The element includes a short term memory for storing received objects in sequential context and long term memories for storing in sequential context previously learned states and predetermined types of knowledge relating to the previously learned states. The element correlates the information stored in the short term memory with information stored in the long term memories for assigning probabilities to possible next states in the sequence of recognized states. The correlation is facilitated by using the context of the information stored in the short term memory as a pointer to the context of the information stored in the long term memories.