Mihai Boicu, Gheorghe Tecuci, Dorin Marcu Learning Agent Center, George Mason University

Transcription

1 Mihai Boicu, Gheorghe Tecuci, Dorin Marcu Learning Agent Center, George Mason University The Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security STIDS Fairfax, VA, 25 October 2012

2 Outline Motivation Hypothesis Analysis Aum Shinrikyo Report Cognitive Assistant Development Capturing the Expertise Applying the Model Conclusion

3 Objective: Building Cognitive Assistants Cognitive Assistants incorporate the knowledge of human experts assist their users in complex problem solving train junior analysts Subject Matter Expert Dialog Knowledge Engineer Programming Cognitive Assistant Inference Engine Knowledge Base Results

4 Teaching as Alternative to Programming Rarely does a technology arise that offers such a wide range of important benefits of this magnitude. Yet as the technology moved through the phase of early adoption to general industry adoption, the response has been cautious, slow, and linear (rather than exponential). Edward Feigenbaum Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child's? Alan Turing If this were then subjected to an appropriate course of education one would obtain the adult brain.

5 Disciple Approach The expert teaches the agent how to solve problems The agent continuously develops and refines its knowledge Learning System Subject Matter Expert Dialog Inference Engine Learning Engine Knowledge Engineer Knowledge Base

6 Hierarchical Knowledge Organization Shared KBs Evidence-based Reasoning Domain KBs Assess WMD programs. Scenario KBs Aum Shinrikyo Roqoppi brigade

7 Knowledge Base = Ontology + Rules Ontology: Hierarchical representation of general and domain concepts Rules: Specified with the concepts from the ontology Reasoning tree: Finding the solution of a specific problem demonstrative tangible tangible unequivocal based upon direct observation real tangible unequivocal unequivocal obtained at second hand based on opinion IF the problem to solve is P IF the authoritative problem to solve is 1g IF the problem record to solve is P 1g IF Condition the problem to solve is P 1g missing IF Condition the problem to solve is P 1g IF Except-When Condition the problem to Condition solve is P 1g IF Except-When Condition the problem to Condition solve is P 1g Except-When IF Condition the problem to Condition solve is P 1g Except-When Condition 1g Condition Except-When Condition Condition Except-When Condition Condition Condition THEN Except-When solve its sub-problems Condition Condition THEN Except-When solve P 1 its P 1 sub-problems Condition Condition THEN Except-When solve 1g ng 1 its sub-problems Condition THEN Except-When solve 1 1g ng P 1 its P 1 sub-problems Condition THEN Except-When solve 1g ng P 1 its P 1 sub-problems Condition THEN solve 1g ng P 1 its P 1 sub-problems THEN solve ng P 1 its P 1 sub-problems THEN solve ng P 1 its P 1 sub-problems ng P 1 P 1 equivocal completely equivocal probabilistically 1g equivocal 1g 1g 1g ng P 1 S 1 S 1 P 1 S 1 P n n S 2 S 2 P P 2 m m S 3 S 3 P Pp 3 p

8 Computational Theory of Intelligence Analysis Generate and analyze hypotheses from masses of : incomplete, inconclusive, ambiguous, dissonant, partially believable, in a world that is changing all the time. Explanatory Hypotheses Likeliness of Hypotheses Evidence marshaling What hypotheses would explain these observations? (Abduction: O possibly H) Hypothesis-driven collection What is entailed by each hypothesis? (Deduction: H necessarily E) Multi-INT fusion What is the -based likeliness of each hypothesis? (Induction: E probably H) Observations Evidence in search of hypotheses Hypotheses in search of New Evidence Evidentiary testing of hypotheses

9 Main Elements Divide and conquer Natural for analyst Appropriate for system Guided by questions Kipling: What, Why, Solutions synthesis Predefined patterns: Believability analysis H 2 1 Rr i H1 1 S 1 1 Question Answer Question Answer S 2 1 H 1 S 1 Question Answer Question Answer H1 3 S1 3 H 2 m Hn 1 Sm 2 Question Answer Question Answer H 3 p S 1 n P 1 S 3 p S 1 Sr j based reasoning P 2 1 S 2 1 P 1 1 S 1 1 P 2 m S 2 m P 1 n S 1 n

10 Assessment Based on Reduction Assess H 1 Problem-level synthesis Likeliness of H 1 Synthesis function Question Q Answer A Partial Likeliness of H 1 Synthesis function Reduction-level synthesis Question Q Answer B Partial Likeliness of H 1 Synthesis function Assess H 2 Assess H 3 Assess H 4 Assess H 5 Likeliness of H 2 Likeliness of H 3 Likeliness of H 4 Likeliness of H 5

11 Hypothesis Assessment Likelihood no possibility a remote possibility very unlikely unlikely an even chance likely very likely almost certain certain Which is a favoring item of? E 1 likely Assess hypothesis H 1 based on favoring almost certain max Inferential Force answers the question: How strong is E 2 in favoring (or disfavoring) H 1? Which is a favoring item of? E 2 almost certain min min Assess relevance of E 1 to H 1 very likely Assess believability of E 1 likely Assess relevance of E 2 to H 1 certain Assess believability of E 2 almost certain Relevance answers the question: So what? May E 1 change my belief in the truthfulness of H 12? Believability answers the question: Can we believe what E 1 is telling us?

12 Acquiring Domain Knowledge from Reports Shared KBs Evidence-based Reasoning Domain KBs Assess WMD programs. Scenario KBs Aum Shinrikyo Roqoppi brigade

13 Aum Shinrikyo Report Aum Shinrikyo Insights Into How Terrorists Develop Biological and Chemical Weapons, Center for a New American Security, July 2011 Radicalization process Chemical weapons Biological weapons This detailed case study of Aum Shinrikyo (Aum) suggests several lessons for understanding attempts by other terrorist groups to acquire chemical or biological weapons.

14 Knowledge Capture Methodology Problem specification Assess whether a terrorist organization is pursuing weapons of mass destruction. Rapid prototyping P 1 S 1 P 1 S 1 QuestionP 1 S 1 Answer Question Question Answer Answer Question Question Answer Answer Question P 1 1 S 1 Answer 1 Pn 1 S 1 n P 1 1 S 1 1 Pn 1 S 1 n P 1 Question 1 S 1 1 Pn 1 S 1 n Answer Question Question Answer Answer Question Question Answer Answer Question P 2 1 S 2 Answer 1 Pm 2 Sm 2 P 2 1 S 2 1 Pm 2 Sm 2 P 2 1 S 2 1 Pm 2 Sm 2 Ontology development Rule learning and ontology refinement Bob Sharp is interested in John Doe is expert in Mathematics Artificial Intelligence area of expertise subconcept-of research area subconcept-of Computer Science instance-of instance-of Biology Software Engineering IF the problem to solve is P IF the problem to solve 1g is P IF the problem to solve 1g is P Condition IF the problem to solve 1g is P Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Except-When Condition Condition 1g Except-When Condition Except-When Condition Condition Except-When Condition Except-When Condition Condition THEN solve its sub-problemsexcept-when Condition Except-When Condition THEN P 1 solve 1 its sub-problemsexcept-when Condition Except-When Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problems 1g THEN ng P 1 solve 1 its sub-problems 1g THEN ng P 1 solve 1 its sub-problems 1g ng P 1 P 1 1g ng

15 Specifying the Hypothesis for Reuse Assess whether Aum Shinrikyo is pursuing sarin-based weapons. Assess whether Aum Shinrikyo is pursuing B-anthracis-based weapons Assess whether?o1 is pursuing?o2. Assess whether Roqoppi brigade is pursuing botulinum-based weapons.

16 Necessary Condition Assess whether Aum Shinrikyo is pursuing sarin-based weapons. has reasons has desire capabilities to acquire capabilities to use

17 Elementary Hypotheses

18 Abstract View

19 Assessment of Elementary Hypotheses

20 Continue the model Assess whether Aum Shinrikyo is pursuing sarin-based weapons. almost certain......

21 Solve the Other Example Assess whether Aum Shinrikyo is pursuing B-anthracis-based weapons

22 Knowledge Capture Methodology Problem specification Assess whether a terrorist organization is pursuing weapons of mass destruction. Rapid prototyping P 1 S 1 P 1 S 1 QuestionP 1 S 1 Answer Question Question Answer Answer Question Question Answer Answer Question P 1 1 S 1 Answer 1 Pn 1 S 1 n P 1 1 S 1 1 Pn 1 S 1 n P 1 Question 1 S 1 1 Pn 1 S 1 n Answer Question Question Answer Answer Question Question Answer Answer Question P 2 1 S 2 Answer 1 Pm 2 Sm 2 P 2 1 S 2 1 Pm 2 Sm 2 P 2 1 S 2 1 Pm 2 Sm 2 Ontology development Rule learning and ontology refinement Bob Sharp is interested in John Doe is expert in Mathematics Artificial Intelligence area of expertise subconcept-of research area subconcept-of Computer Science instance-of instance-of Biology Software Engineering IF the problem to solve is P IF the problem to solve 1g is P IF the problem to solve 1g is P Condition IF the problem to solve 1g is P Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Condition IF the problem to solve 1g is P Except-When Condition Except-When Condition Condition 1g Except-When Condition Except-When Condition Condition Except-When Condition Except-When Condition Condition THEN solve its sub-problemsexcept-when Condition Except-When Condition THEN P 1 solve 1 its sub-problemsexcept-when Condition Except-When Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problemsexcept-when Condition 1g THEN ng P 1 solve 1 its sub-problems 1g THEN ng P 1 solve 1 its sub-problems 1g THEN ng P 1 solve 1 its sub-problems 1g ng P 1 P 1 1g ng

23 Ontology Specification and Development expertise actor microbiology bacteriology person organization terrorist group virology chemistry Aum Shinrikyo has master degree in has as member Masami Tsuchiya

24 Rule Learning Method Specific reduction H 1 1 H 1 Question Q Answer A H 1 n Explanation Partially learned rule IF the problem to solve is P 1g Question pattern Q g Answer pattern A g Main condition Plausible upper bound condition Plausible lower bound condition Ontology demonstrative tangible tangible unequivocal based upon direct observation real tangible unequivocal unequivocal obtained at second hand based on opinion missing equivocal completely equivocal authoritative record probabilistically equivocal o 1 o 2 f 1 o 3 f 2 THEN solve its sub-problems P 1 P 1 Minimally generalized example and explanation 1g ng

25 Learn General Rules

26 Rule Refinement Incorrect reduction P 1a 1 P a 1 Question Q Answer A P 1a n Refined rule IF the problem to solve is P 1g Question pattern Q g Answer pattern A g Main condition Plausible upper bound condition Plausible lower bound condition Ontology demonstrative tangible tangible unequivocal based upon direct observation real tangible unequivocal unequivocal obtained at second hand based on opinion missing equivocal completely equivocal authoritative record probabilistically equivocal Failure Explanation f 3 o4 o 5 Except-when condition Plausible upper bound condition Plausible lower bound condition THEN solve its sub-problems P 1 P 1 1g ng Minimally generalized examples and explanations

27 Using the Cognitive Assistant Assess whether Roqoppi brigade is pursuing botulinum-based weapons.

28 Conclusion Use of analyses reports Better utilization of the expert resources and time Cost effective Use of cognitive assistants offers: Better dissemination of the results Training junior analysts on the job Source materials Lessons learned documents After-action reports Diagnostic reports

29 Questions