Mihai Boicu, Gheorghe Tecuci, David Schum Disciple-LTA is a new type - - PowerPoint PPT Presentation

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Mihai Boicu, Gheorghe Tecuci, David Schum Disciple-LTA is a new type - - PowerPoint PPT Presentation

Mihai Boicu, Gheorghe Tecuci, David Schum Disciple-LTA is a new type of personal cognitive assistant that: can rapidly acquire expertise in intelligence analysis, can train new intelligence analysts, and can help analysts analyze


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Mihai Boicu, Gheorghe Tecuci, David Schum

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Disciple-LTA is a new type of personal cognitive assistant that:

  • can rapidly acquire expertise in intelligence analysis,
  • can train new intelligence analysts, and
  • can help analysts analyze complex hypotheses

 through mixed-initiative reasoning,  allowing a synergistic integration of a human’s experience and

creativity with an agent’s knowledge and speed, and

 facilitating collaboration with complementary experts and their

agents.

  • Rapid and systematic hypothesis analysis
  • Assumption-based reasoning
  • Evidence retrieval and representation
  • Automatic report generation
  • Collaboration and information sharing
  • Rapid acquisition of analytic expertise
  • Learning to analyze based on source’s culture
  • Tutoring new analysts
  • Automatic test generation
  • Easy integration with other tools and services
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The expert teaches Disciple how to solve problems in a way that resembles how the expert would teach a student, 
 an apprentice or a collaborator. Disciple continuously develops and refines its knowledge base to capture and better represent expert’s knowledge and problem solving strategies. D I S C I P L E There is no longer a distinction between knowledge base development and its maintenance.

Develop learning and problem solving agents that can be taught by subject matter experts to become knowledge-based assistants.

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1.

A complex hypothesis analysis problem is successively reduced to simpler problems that either have known solutions or can be solved through evidence analysis.

2.

Potentially relevant pieces

  • f

evidence for the unsolved problems are identified.

3.

The pieces

  • f

evidence are analyzed to obtain solutions for the unsolved problems.

4.

The solutions of the simplest problems are successively combined to obtain the solution

  • f the initial problem.

S1 S11 S1n S111 S11m P11m P111 P1n P11 P1 … … Sa

11m

Sd

11m

Pd

11m

Pa

11m

… Assess whether Al Qaeda has nuclear weapons. It is unlikely that Al Qaeda has nuclear weapons. National Intelligence Council’s standard estimative language

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TOC Reduction of a problem to its main sub-problems Abstract tree Detailed tree

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The ontology contains a hierarchical description

  • f the domain concepts.
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The rules specify general reduction

  • r synthesis steps

described with the concepts from the

  • ntology.

ANALYSIS TREE

S1 S11 S1n S111 S11m P11m P111 P1n P11 P1 … …

To assess whether there are states that may be willing to sell nuclear weapons to an actor, one has to consider each nuclear state which is not an enemy of that actor and does not

  • ppose the

proliferation of nuclear weapons, and determine whether that state may be willing to sell nuclear weapons to that actor.

Partially learned rule

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 knowledge representation  user-agent communication  problem solving  knowledge acquisition and learning

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Testimonial evidence based upon direct observation EVD-Dawn-Mir01-02c Equivocal testimonial evidence

  • bservational sensitivity

credibility credential

  • bjectivity

veracity has credibility credential

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EVD‐ Wallflower‐5 EVD‐ Wallflower‐4 EVD‐ Wallflower‐3 EVD‐ Wallflower‐2 EVD‐ Wallflower‐1 recording transla:on edi:ng transmission Wallflower’s tes:mony about Emir Z in Farsi. Wallflower’s recorded tes:mony about Emir Z in Farsi. Wallflower’s translated tes:mony about Emir Z in English. Wallflower’s reported tes:mony about Emir Z in English. Wallflower’s received tes:mony about Emir Z in English. Clyde Wallflower

  • Competence
  • Credibility

Bob

  • Competence
  • Veracity

Sony Recorder

  • Fidelity
  • Reliability

Husam A.

  • Competence
  • Credibility

Marsha

  • Competence
  • Veracity

SN 247

  • Fidelity
  • Reliability
  • Security

Marsha

  • Competence
  • Credibility
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A statement made by Osama bin Laden

 represents his view  should not interfere with other (maybe

contradictory) statements made by other sources,

  • r by the analyst who taught Disciple.
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Dawn-Mir01 Dawn-Mir01-b has as content has as source states as view Osama bin Laden is view of

testimonial evidence instance of

has as author Hamid Mir Al Qaeda in-Dawn-Mir01-b weapons 1 in-Dawn-Mir01-b

group nuclear weapons

Al Qaeda

newspaper article

instance of states as view instance of instance of uses as deterrent

terrorist group

instance of has as weapons

elementary piece of evidence Osama bin Laden statement

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Automatically generated illustration of the abstract strategy Abstract reduction strategy

Lesson Fragment: Hypothesis support from a piece of evidence

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Veracity

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Disciple-LTA Disciple-LTA Clients Clients Hypothesis Hypothesis Analysis Analysis Broker Broker Hypothesis Hypothesis Analysis Analysis Web Service Web Service Disciple-LTA Disciple-LTA Client Client

Hypothesis: Assess whether Al Qaeda has nuclear weapons.

Disciple LTA Disciple LTA Servers Servers

Sub-hypothesis: Assess whether Al Qaeda makes credible claims to have nuclear weapons.

2

Ask broker for solutions

5

Sub-hypothesis: Assess whether other countries within the global community believe that Al Qaeda has nuclear weapons.

UDDI UDDI

Receive solutions

3

Check UDDI for registered competence

1

Perform analysis

6

Integrate Solutions

7

Upload reasoning tree in Catalyst

Sub-hypothesis: Assess whether other countries within the global community believe that Al Qaeda has nuclear weapons. Solution: It is likely that other countries within the global community believe that Al Qaeda has nuclear weapons.

4

Solicit / receive solutions

Sub-hypothesis: Assess whether Al Qaeda makes credible claims to have nuclear weapons. Solution: It is almost certain that the Al Qaeda claims of having nuclear weapons are credible.

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  • 1. The analyst extends

the analysis logic

Learned Rule Learned Rule

  • 2. Disciple learns

reasoning rules

To assess whether there are states that may be willing to sell nuclear weapons to an actor, one has

  • ne has

to consider to consider each nuclear each nuclear state state and assess whether that state may be willing to sell nuclear weapons to that actor.

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This is wrong! France will not sell nuclear weapons to Al Qaeda because it perceives it as an enemy.

  • 3. Disciple-LTA refines

the rule with an except- when condition

To assess whether there are states that may be willing to sell nuclear weapons to an actor, one

  • ne has to consider each

has to consider each nuclear state nuclear state and assess whether that state may be willing to sell nuclear weapons to that actor, except for except for the case in which the nuclear state is an enemy of that actor.

Refined Rule

  • 1. Disciple applies the learned rule
  • 2. The analyst

critiques the reasoning

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IF IF <Problem> <Problem> THEN THEN <Subproblem 1> <Subproblem 1> … … <Subproblem m> <Subproblem m> PVS Condition PVS Condition Except-When Except-When PVS Condition PVS Condition

direct direct test ev test ev

Continuous Continuous adaptation of adaptation of the previously the previously learned rules learned rules to the to the evolution of evolution of the ontology. the ontology.

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Analytic Assistance

Empowers the analysts through mixed-initiative reasoning for hypotheses analysis, collaboration with other analysts and experts, and sharing of information.

Learning

Rapid acquisition and maintenance

  • f subject matter

expertise in intelligence analysis which currently takes years to establish, is lost when experts separate from service, and is costly to replace.

Tutoring

Helps new intelligence analysts learn the reasoning processes involved in making intelligence judgments and solving intelligence analysis problems.

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Disciple Learning and Tutoring Shell

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Agent Learning Theory

Critical requirement for a new generation

  • f cognitive assistants

Domain Knowledge Base Tutoring Knowledge Base

Mixed-initiative reasoning Multistrategy learning Reasoning with uncertainty Expertise modeling

STTR Phase II