Intelligence analysis method and system using subjective logic
First Claim
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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Accused Products
Abstract
Method of and system for analysing a set of exhaustive and exclusive hypotheses, including assessing and assigning base rates for each hypothesis; determining a set of items of evidence that are relevant to, have a causal influence on, or would disconfirm more than one hypothesis; assessing and assigning base rates for each item of evidence; 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; if the item of evidence is to be treated as a causal influence—making a judgement as to the likelihood of each hypothesis, both if the evidence were true, and also if the evidence were false; if the item of evidence is to be treated as a diagnostic indicator—making a judgement as to the evidence being true if the hypothesis were true; assessing the belief for each item of evidence being true; deciding a set of interim beliefs in each hypothesis for each individual item of evidence by:
- employing a conditional inference operator for evidence that is to be treated as a causal influence; and
- employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; and
deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs.
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Citations
21 Claims
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1. A computer implemented method of analysis of competing hypotheses in estimative intelligence, said method comprising the steps of:
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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, and B. 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for analyzing a set of exhaustive and exclusive hypotheses in estimative intelligence, said system comprising:
at least one processor for executing instructions, a memory coupled to the processor, a data storage system for reading media having sequences of instructions stored thereon coupled to the data storage system, input/output for delivering data to and from the memory or to and from the data storage system and a user interface allowing for interaction with said instruction sequences by users, which sequences also causes said at least one processor to execute the steps of; assessing and assigning base rates for each hypothesis; determining a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; assessing and assigning base rates for each item of evidence; 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; if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis, both if the evidence were true, and also if the evidence were false; if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the evidence being true if the hypothesis were true; assessing the belief for each item of evidence being true; deciding a set of interim beliefs in each hypothesis for each individual item of evidence by; employing a conditional inference operator for evidence that is to be treated as a causal influence; and employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and outputting a set of beliefs representing the certainty and likelihood of each hypothesis. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer program product for analysis of competing hypotheses in estimative intelligence, the computer program product comprising:
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computer-readable storage media having computer-readable sequences of instructions stored on the computer-readable storage media, wherein the sequences of instructions comprise; first instructions configured to decide on a plurality of possible hypotheses to be considered; second instructions configured to identify significant items of evidence for and against each of said plurality of hypotheses; third instructions configured to construct 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, and B. if the evidence were false; and 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; fourth instructions configured to access the belief for each item of evidence being true; fifth instructions configured to decide 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; sixth instructions configured to decide the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and seventh instructions configured to output a set of beliefs representing the certainty and likelihood of each hypothesis.
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Specification