Cognitive arbitration system
First Claim
1. A machine, having a memory comprising data representing a plurality of hypotheses and associated support values for the plurality of hypotheses, the data being a product of a process comprising the steps of:
- generating candidate outputs, representing an input pattern, at a plurality of recognizers;
assigning fuzzy membership states to respective candidate outputs of the plurality of recognizers;
selecting at least one rule according to the assigned fuzzy membership states, a selected rule providing a determined amount of support mass for an associated one of the plurality of hypotheses and a determined amount of uncertainty mass;
determining a support value for each hypothesis and an overall uncertainty value from the provided mass values;
comparing the support value for the selected hypothesis to a first threshold value;
comparing the overall uncertainty value to a second threshold value;
accepting the selected hypothesis if the support value meets the first threshold and the overall uncertainty value is below the second threshold value; and
outputting a negative result if the selected hypothesis is not accepted.
1 Assignment
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Accused Products
Abstract
A method and computer product is disclosed for arbitrating the outputs of a plurality of recognizers to select a hypothesis associated with a given input from a plurality of hypotheses. Fuzzy membership states are assigned to respective candidate outputs of the plurality of recognizers. At least one rule is selected according to the assigned fuzzy membership states. A selected rule provides a determined amount of support mass for an associated one of the plurality of hypotheses and a determined amount of uncertainty mass. A support value for each hypothesis and an overall uncertainty are determined from the provided mass values. A hypothesis having a highest support value while having an acceptable low level of uncertainty is selected.
24 Citations
22 Claims
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1. A machine, having a memory comprising data representing a plurality of hypotheses and associated support values for the plurality of hypotheses, the data being a product of a process comprising the steps of:
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generating candidate outputs, representing an input pattern, at a plurality of recognizers; assigning fuzzy membership states to respective candidate outputs of the plurality of recognizers; selecting at least one rule according to the assigned fuzzy membership states, a selected rule providing a determined amount of support mass for an associated one of the plurality of hypotheses and a determined amount of uncertainty mass; determining a support value for each hypothesis and an overall uncertainty value from the provided mass values; comparing the support value for the selected hypothesis to a first threshold value; comparing the overall uncertainty value to a second threshold value; accepting the selected hypothesis if the support value meets the first threshold and the overall uncertainty value is below the second threshold value; and outputting a negative result if the selected hypothesis is not accepted. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-readable medium operative in a data processing system, that stores executable instructions for arbitrating the outputs of a plurality of recognizers to select a hypothesis for a given input from a plurality of hypotheses, the executable instructions comprising:
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a fuzzy class assignment routine that assigns a fuzzy membership state to respective at least one candidate outputs of the plurality of recognizers; a rule selection routine that evaluates a plurality of rules according to the assigned fuzzy membership states of the candidate outputs to determine which of the plurality of rules apply to each of the plurality of output hypotheses, an applicable rule providing a determined amount of support mass for an associated hypothesis and a determined amount of uncertainty mass; and a data fusion routine that determines a support value for each hypothesis and an overall uncertainty value from the provided mass values, normalizes the support values for the plurality of hypotheses to determine associated belief and plausibility values for each of the plurality of hypotheses, and selects a hypothesis having a highest support value; such that a general purpose processor can execute the executable instructions stored on the computer readable medium to select a hypothesis for a given input from a plurality of available hypotheses. - View Dependent Claims (11, 12, 13)
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14. A system for classifying an input pattern into one of a plurality of output classes comprising:
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a memory comprising executable instructions, the executable instructions comprising; a plurality of pattern recognition routines, a given pattern recognition routine selecting at least one of the plurality of output classes and assigning respective output scores to the selected at least one output class; at least one fuzzy class routine that assigns a fuzzy membership state to the selected output classes of the plurality of pattern recognition routines; a rule selection routine that evaluates a plurality of rules according to the assigned fuzzy membership states to determine which of the plurality of rules apply to each of a plurality of output hypotheses, an applicable rule providing a determined amount of support mass for an associated hypothesis and a determined amount of uncertainty mass; and a data fusion routine that multiplies the associated support masses for a plurality of selected rules associated with each hypothesis to form a support value for the hypothesis and multiplying the associated uncertainty mass for the plurality of selected rules associated with each hypothesis to form an uncertainty value for the hypothesis and selects an output class associated with a hypothesis having a highest support value; and a processor for executing the executable instructions as to classify the input pattern into one of the plurality of output classes. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
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Specification