A probabilistic learning element particularly adapted for use as a task independent sequential pattern recognition device receives sequences of objects and outputs sequences of recognized states composed of objects and includes a plurality of memories for storing the received sequences of objects and previously learned states as well as predetermined types of knowledge relating to previously learned states. The sequences of received objects are correlated with the information relating to the previously learned states in order to assign probabilities to possible next states in the sequence of recognized states. 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 stated 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