INTELLIGENCE ANALYSIS METHOD AND SYSTEM

• US 20070288418A1
• Filed: 06/10/2006
• Published: 12/13/2007
• Est. Priority Date: 06/10/2006
• Status: Active Grant

• Current Assignee: Veriluma Pty Limited
• Original Assignee: Distip Pty Limited
• Inventors: McAnally, David Scott, Pope, Simon Kevin John, Josang, Audun, McAnally, Rayma Judith
• Granted Patent: US 7,720,787 B2
• Time in Patent Office: Days
• US Class Current: 706/53
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

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.

Claims

1. A method of analysis of competing hypotheses, said method comprising the steps of:

a. deciding on possible hypotheses to be considered;

b. identifying significant items of evidence for and against each hypothesis;

c. constructing a model for analysing 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 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 judgement 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 judgement as to 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; and

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

2. The analysis method of claim 1 wherein the step of identifying the set of significant items of evidence is not limited to what evidence is already known or believed to be discoverable.

3. The analysis method of claim 1 wherein the steps of assigning base rates to hypotheses and to the items of evidence involves assigning prior probabilities to each hypothesis and to each item of evidence, respectively.

4. The analysis method of claim 1 wherein competing hypotheses in the set are alternative explanations of the evidence.

5. The analysis method of claim 1 wherein the evidence includes possible causal influences and diagnostic indicators for one or more of the hypotheses.

6. The analysis method of claim 1 wherein the belief for an item of evidence or hypothesis being true includes both the certainty and likelihood of the item of evidence being true.

7. The analysis method of claim 6 wherein the likelihood of the belief an item of evidence or hypothesis being true expresses the probability of the item of evidence being true.

8. The analysis method of claim 6 wherein the certainty of the belief an item of evidence or hypothesis being true expresses the strength of the claim about the assigned likelihood being accurate.

9. The analysis method of claim 1 wherein deciding the belief in each interim hypothesis for each item of evidence is considered apart from the general set of evidence under consideration in relation to the hypothesis.

10. The analysis method of claim 1 wherein the model is comprised of a set of conditionals for all items of evidence and the hypotheses, wherein said model can be used to evaluate a complete or incomplete set of evidence by inputting a set of observable evidence together with expressions of vacuous beliefs where no data is available, whereby the model outputs a set of beliefs representing the certainty or likelihood of each hypothesis.

12. A computer readable medium having stored thereon one or more sequences of instructions for causing one or more processors to execute the steps of the method recited in claim 1.

11. A method of analysing a set of exhaustive and exclusive hypotheses, said method comprising 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 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.

14. A computer readable medium having stored thereon one or more sequences of instructions for causing one or more processors to execute the steps of the method recited in claim 11.

13. A system for analysing a set of exhaustive and exclusive hypotheses, said system comprising at least one processor for executing instructions, memory means coupled to the processor, storage means for reading media having sequences of instructions stored thereon coupled to the storage means, input/output means for delivering data to and/or from the memory or storage means and user interface means allowing for interaction with said instruction sequence, which sequence 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 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.