4593367 : Probabilistic learning element

ABSTRACT:   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.

U.S. REFERENCES:

34 patents reference this one

Patent No. Inventor Issued Title 3103648 * Hartmanis 9 /1963   3196399 * Kamentsky 7 /1965   3267431 * Greenberg 8 /1966   3414885 * Muller 12 /1968   3440617 * Lesti 4 /1969   3446950 * King, Jr. 5 /1969   3457552 * Asendorf 7 /1969   3562502 * Kautz 8 /1967 CELLULAR THRESHOLD ARRAY FOR PROVIDING OUTPUTS REPRESENTING A COMPLEX WEIGHTING FUNCTION OF INPUTS 3581281 Martin 5 /1971 PATTERN RECOGNITION COMPUTER 3588823 Chow 6 /1971   3601811 Yoshino 8 /1971 LEARNING MACHINE 3613084 Armstrong 10 /1971 TRAINABLE DIGITAL APPARATUS 3623015 Schmitz et al. 11 /1971 STATISTICAL PATTERN RECOGNITION SYSTEM WITH CONTINUAL UPDATE OF ACCEPTANCE ZONE LIMITS 3638196 Nishiyama et al. 1 /1972 LEARNING MACHINE 3646329 Yoshino et al. 2 /1972 ADAPTIVE LOGIC CIRCUIT 3678461 Choate et al. 7 /1972 EXPANDED SEARCH FOR TREE ALLOCATED PROCESSORS 3700866 Taylor 10 /1972 SYNTHESIZED CASCADED PROCESSOR SYSTEM 3701974 Rusell 10 /1972 LEARNING CIRCUIT 3702986 Taylor et al. 11 /1972 TRAINABLE ENTROPY SYSTEM 3715730 Smith et al. 2 /1973 MULTI-CRITERIA SEARCH PROCEDURE FOR TRAINABLE PROCESSORS 3716840 Masten et al. 2 /1973 MULTIMODAL SEARCH 3725875 Choate et al. 4 /1973 PROBABILITY SORT IN A STORAGE MINIMIZED OPTIMUM PROCESSOR 3753243 Ricketts, Jr. et al. 8 /1973 PROGRAMMABLE MACHINE CONTROLLER 3772658 Sarlo 11 /1973 ELECTRONIC MEMORY HAVING A PAGE SWAPPING CAPABILITY 3934231 Armstrong 1 /1976 Adaptive boolean logic element 3950733 Cooper et al. 4 /1976 Information processing system 3988715 Mullan et al. 10 /1976 Multi-channel recognition discriminator 3999161 van Bilzem et al. 12 /1976 Method and device for the recognition of characters, preferably of figures 4066999 Spanjersberg 1 /1978 Method for recognizing characters 4100370 Suzuki et al. 7 /1978 Voice verification system based on word pronunciation 4189779 Brautingham 2 /1980 Parameter interpolator for speech synthesis circuit 4286330 Isaacson 8 /1981 Autonomic string-manipulation system 4318083 Argyle 3 /1982 Apparatus for pattern recognition 4384273 Ackland et al. 5 /1983 Time warp signal recognition processor for matching signal patterns 4450530 Llinas et al. 5 /1984 Sensorimotor coordinator 4504970 Werth et al. 3 /1985 Training controller for pattern processing system 4507760 Fraser 3 /1985 First-in, first-out (FIFO) memory configuration for queue storage   * some details unavailable EXEMPLARY CLAIM(s): Show all 14 claims What is claimed is: 1. A probabilistic learning element, that sequentially receives objects and outputs sequences of recognized states, said learning element comprising: means for sequentially receiving objects; means for storing, said received objects, sequences of received objects, previously learned sequences of states, states contained in said previously learned sequences of states, and predetermined types of knowledge relating to, said previously learned sequences of states, said states contained in said previously learned sequences of states, objects contained in said previously learned sequences of states, and sequences of objects contained in said previously learned sequences of states, whereby current object information relating to said received objects and said sequences of received objects is stored as well as statistical information relating to previously learned sequences of states and said states, objects and sequences of objects contained in said previously learned sequences of states; means for correlating said stored current object information with said stored statistical information for assigning probabilities to possible next states in the sequence of recognized states; means, responsive to said probabilities of possible next states, for determining a most likely next state; means, responsive to the stored current object information and statistical information, for providing a signal corresponding to the 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. RELATED U.S. APPLICATIONS: none FOREIGN APPLICATION PRIORITY DATA: none FOREIGN REFERENCES: none OTHER REFERENCES: Roberts, "Artificial Intelligence" BYTE, Sep. 1981, 164-178. Jackson, Jr., "Introduction to Artificial Intelligence" Petrocelli, New York 1974. Healy, "Machine Intelligence and Communications in Future NASA Missions", IEEE Communications, vol. 19, No. 6, pp. 8-15. Bennett, Jr. "How Artificial is Intelligence", American Scientist, vol. 65, pp. 694-702. PRIMARY/ASSISTANT EXAMINERS: Smith; Jerry; Grossman; Jon D. ADDED TO DATABASE: Aug. 22, 1996