4620286 : Probabilistic learning element

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. The memory for storing learned information is a context organized memory including a plurality of tree structures having various types of information stored in nodes thereof with certain of the tree structures including at each node an attribute list referring to other tree structures whereby searching is facilitated and unnecessary searching eliminated. The element derives support coefficients relating to how much information was available when calculating conditional probabilities and support coefficients and conditional probabilities are combined to provide a rating of confidence. When the rating of confidence exceeds a predetermined level, the element is caused to store the outputted recognized state sequence as a learned state sequence with the memories storing various types of knowledge relating to the learned sequence of states.

U.S. REFERENCES:

25 patents reference this one

Patent No. Inventor Issued Title 3103648 * Hartmanis 9 /1963   3196399 * Kamentsley et al. 7 /1965   3267431 * Greenberg et al. 8 /1966   3414885 * Muller 12 /1968   3440617 * Lesti 4 /1969   3446950 * King, Jr. et al. 5 /1969   3457552 * Asendorf 7 /1969   3562502 Kautz 2 /1971 CELLULAR THRESHOLD ARRAY FOR PROVIDING OUTPUTS REPRESENTING A COMPLEX WEIGHTING FUNCTION OF INPUTS 3566359 Connelly 2 /1971   3576976 Russo 5 /1971 NONLINEAR OPTIMIZING COMPUTER FOR PROCESS CONTROL 3581281 Martin 5 /1971 PATTERN RECOGNITION COMPUTER 3588823 Chow et al. 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 Russell 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. 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 Bilizem et al. 12 /1976 Method and device for the recognition of characters, preferably of figures 4066999 Spanjersberg 1 /1978 Method for recognizing characters 4189779 Brautingham 2 /1980 Parameter interpolator for speech synthesis circuit 4286330 Isaacson 8 /1981 Autonomic string-manipulation system 4318083 Argule 3 /1982 Apparatus for pattern recognition 4384273 Ackland et al. 5 /1983 Time warp signal recognition processor for matching signal patterns 4450530 Uinas 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 41 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 received object information, including, said received objects, and sequences of received objects; means for storing items of previously learned information, said items including, sequences of states, states contained in said sequences of states, objects contained in said states contained in said sequences of states, sequences of objects contained in said states contained in said sequences of states, positional information for each object contained in said states contained in said sequences of states, and predetermined types of knowledge relating to said previously learned information, whereby received object information, relating to received objects, is stored as well as previously learned information; means for correlating said received object information with said previously learned information for assigning conditional probabilities to possible sequencies of recognized states; means, responsive to said conditional probabilities of possible sequences of recognized states, for determining a most likely sequence of recognized states; means, responsive to said previously learned information, for detecting that a state has ended and for providing an end of state signal; and means, responsive to said end-of-state signal, for outputting said most likely sequence of recognized states as a recognized state sequence. RELATED U.S. APPLICATIONS: none FOREIGN APPLICATION PRIORITY DATA: none FOREIGN REFERENCES: none OTHER REFERENCES: Artificial Intelligence, Roberts BYTE, pp. 164-178, Sep. 1981. Introduction to Artificial Intelligence, Jackson, pp. 368-380, Petrocelli Charter, New York, 1974. "Machine Intelligence and Communications in Future NASA Missions", Healy, IEEE Communications, pp. 8-15, Nov. 1981. "How Artificial is Intelligence?", Bennet, Jr.; American Scientist, pp. 694-702, vol. 65, No. 6, Nov.-Dec. 1977. PRIMARY/ASSISTANT EXAMINERS: Smith; Jerry; Grossman; Jon D. ADDED TO DATABASE: Sep. 24, 1997