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. Said 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. Based upon the probabilities of the possible next states the most likely next state is determined and outputted as a recognized next state in the recognized state sequence when the element determines that a state has ended. The element additionally includes means for providing a rating of confidence in the recognized next state. The ratings of confidence for a sequence of recognized states are accumulated and if the accumulated value exceeds a predetermined threshold level the element will be caused to store the recognized state sequence as a learned state sequence.
PROBABILISTIC LEARNING ELEMENT EMPLOYING CONTEXT DRIVEN SEARCHING