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Intelligence analysis method and system using subjective logic

  • US 7,720,787 B2
  • Filed: 06/10/2006
  • Issued: 05/18/2010
  • Est. Priority Date: 06/10/2006
  • Status: Active Grant
First Claim
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1. A computer implemented method of analysis of competing hypotheses in estimative intelligence, said method comprising the steps of:

  • a. deciding on a plurality of possible hypotheses to be considered;

    b. identifying significant items of evidence for and against each of said plurality of hypotheses;

    c. configuring a processor to construct and store onto a memory a model for analyzing the hypotheses by;

    i. producing a set of exhaustive and exclusive hypotheses, wherein only one hypothesis may be true;

    ii. assessing and assigning base rates for each hypothesis;

    iii. determining from said significant items of evidence identified in relation to respective hypotheses a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis;

    iv. assessing and assigning base rates for each item of evidence;

    v. deciding for each item of evidence whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses;

    vi. if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis;

    A. if the evidence were true, andB. if the evidence were false;

    vii. if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the likelihood of the evidence being true;

    A. if the hypothesis were true;

    d. assessing the belief for each item of evidence being true;

    e. deciding a set of interim beliefs in each hypothesis for each individual item of evidence by;

    i. employing a conditional inference operator for evidence that is to be treated as a causal influence; and

    ii. employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator;

    f. deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and

    g. outputting a set of beliefs representing the certainty and likelihood of each hypothesis.

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