1. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states and includes context driven searching, said learning element comprising: means for sequentially receiving objects; long term memory means for storing in sequential context, previously learned states, objects contained in said previously learned states, and predetermined types of knowledge relating to said stored previously learned states and said objects contained in said previously learned states, whereby from any stored information in said long term memory means the stored information which occurs next in context is directly accessible; short term memory means for storing in sequential context said received objects; means for correlating said received objects stored in said short term memory means with information stored in said long term memory means, said correlation being facilitated by using the context of said objects stored in said short term memory means as a pointer to the context of said information stored in said long term memory means, said correlating means assigning probabilities to possible next states in a sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to said objects stored in said short term memory means and said information stored in said long term memory means, for providing a signal corresponding to a probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence.
1. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states and includes context driven searching, said learning element comprising: means for sequentially receiving objects; long term memory means for storing in sequential context, previously learned states, objects contained in said previously learned states, and predetermined types of knowledge relating to said stored previously learned states and said objects contained in said previously learned states, whereby from any stored information in said long term memory means the stored information which occurs next in context is directly accessible; short term memory means for storing in sequential context said received objects; means for correlating said received objects stored in said short term memory means with information stored in said long term memory means, said correlation being facilitated by using the context of said objects stored in said short term memory means as a pointer to the context of said information stored in said long term memory means, said correlating means assigning probabilities to possible next states in a sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to said objects stored in said short term memory means and said information stored in said long term memory means, for providing a signal corresponding to a probability that a state has ended; and means, responsive to said end of state signal, for outputting said most likely next state as a recognized next state in a recognized state sequence. 4. A probabilistic learning element as described in claim 1, additionally comprising learning supervision means, responsive to external reinforcement signals, for causing said long term memory means to store said recognized states, objects contained in said recognized states and the predetermined types of knowledge relating to said recognized states and said objects contained in said recognized states as previously learned states and information relating to previously learned states.