gheorghe tecuci mihai boicu, dorin marcu, david schum, kathryn … · invited talk the 9th int....

Invited Talk

The 9th Int. Conference on Machine Learning and Applications

ICMLA, Washington DC, USA, 12-14 December 2010

Gheorghe Tecuci

Mihai Boicu, Dorin Marcu, David Schum, Kathryn Russell

Learning Agent Center, George Mason University

2010, Learning Agents Center 2

Abstract

We present the development and applications of Disciple cognitive

agents that integrate three complementary capabilities:

(1)They are able to learn, directly from their users, their subject matter

expertise which currently takes years to establish, is lost when

experts separate from service, and is costly to replace;

(2)They can tutor new users in expert problem solving; and

(3)They can assist their users to solve complex problem in uncertain

and dynamic environments.

We first present how one can teach a Disciple agent to solve a familiar

problem: How to assess a PhD Advisor?

Then we discuss the development of a Disciple agent for the highly

complex domain of intelligence analysis, where it helps intelligence

analysts to discover and evaluate evidence and hypotheses, by

generating Wigmorean probabilistic inference networks that link evidence

to hypotheses in argumentation structures that establish and defend the

relevance, the believability, and the inferential force or weight of evidence.


Overview

Final Remarks

How a Disciple Agent is Taught and Learns

Research and Development Objectives

Cognitive Assistant for Intelligence Analysis:

Learning, Tutoring, and Analytic Assistance


“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).”

Building Expert Systems (Cognitive Agents)

Edward

Feigenbaum

Tiger in a Cage:

The Applications

of Knowledge-

Based Systems

AAAI 1993

Invited Talk

Knowledge

Engineer

Subject Matter

Expert

Knowledge Base

Inference Engine

Expert System

Programming

Dialog

Results

Building expert systems is hard because much of

expert’s problem solving knowledge is in the form

of tacit knowledge which is very difficult to capture.


Alan Turing

Computing

Machinery and

Intelligence

Mind, 59,

433-460, 1950.

Building an intelligent machine by

programming is too difficult.

“Instead of trying to produce a

program to simulate the adult

mind, why not rather try to produce

one which simulates the child's?

If this were then subjected to an

appropriate course of education

one would obtain the adult brain.”

Teaching as Alternative to Programming


The expert

teaches the agent

how to solve

problems in a way

that resembles

how the expert

would teach a

student,

an apprentice or

a collaborator.

The agent

continuously

develops and

refines its

knowledge base to

capture and better

represent expert’s

knowledge and

problem solving

strategies.

Disciple Approach to Agent Development

Approach to develop learning and problem solving agents that can

be taught by subject matter experts to become cognitive assistants.

Main Ideas

Mixed-initiative problem solving

Evidence-based reasoning

Teaching and learning

Multistrategy learning

Resulting agent capabilities

Learning expert knowledge

Assisting experts and non-

experts in problem solving

Teaching students

Disciple Shell and Disciple Cognitive Assistants

New paradigm for

system development

and maintenance

KB

Disciple

Agent

Shell KB

Disciple

Agent

Shell KB

Disciple

Agent

Shell

Disciple↔Expert

Disciple Agent Shell

2010, Gheorghe Tecuci, Learning Agents Center 8

Course of Action Critiquing

Workaround Reasoning in Planning

Sample Applications of the Disciple Agents

Strategic Center of Gravity Determination

PhD Advisor Assessment, Web Believability Evaluation

Higher Order Thinking Skills in History and Statistics

Regulatory Compliance in Financial Services Industry

Medical Triage and Medical Diagnosis

Collaborative Emergency Response Planning

Inquiry-based Learning, Evidence-based Teaching Evaluation

Intelligence Analysis and Evidence-based Reasoning


Vision: Evolution of Software Development and Use

Mainframe

Computers

Software systems

developed and used

by computer experts

Personal

Computers

Software systems

developed by computer

experts and used by

persons who are not

computer experts

Learning

Assistants

Software systems

developed and used by

persons who are not

computer experts

DISCIPLE


Vision: Use of Disciple Agents in Education

teaches Disciple

Agent KB

A subject matter expert teaches

Disciple similarly to how the

expert would teach a student.

teaches Disciple

Agent KB

Disciple

Agent KB

Disciple behaves as a tutoring

system, guiding the student through

a series of lessons and exercises.

Personalized Learning: Grand Challenge for the 21st Century

US National Academy of Engineering, 2008

collaborate

A student uses Disciple as an

assistant and learns from its

explicit reasoning.

Army Intelligence Center


Overview

Final Remarks






A problem P1 is solved by:

• successively reducing it to simpler and simpler problems;

• finding the solutions of the simplest problems;

• successively combining these solutions to obtain the

solution of the initial problem.

Reasoning Model: Divide and Conquer

One of the most highly developed skills in

contemporary Western civilization is dissection;

the split-up of problems into their smallest

possible components. We are good at it. So

good, we often forget to put the pieces back

together again. Alvin Toffler

S3 1 S3

p

S1

S1 n

Question Answer

S1 1

S2 1

Question Answer

S2 m

Question Answer

P1

"I Keep Six Honest...“ by Rudyard Kipling

I keep six honest serving-men

(They taught me all I knew);

Their names are What and Why and When

And How and Where and Who.

P1 1

P1 n

Question Answer

P2 1 P2

m

Question Answer

P3 p P3

1

Question Answer

Problem Reduction and

Solution Synthesis


PhD Advisor Assessment: Overall Logic

Yves

Kodratoff

PhD Advisor

…

Necessary conditions satisfied? Yes

Assess whether John Doe is

a good PhD advisor for Bob

Sharp in Artificial Intelligence.

Assess whether John Doe is a

potential PhD advisor for Bob Sharp.

Assess whether John Doe

would be a good PhD advisor

for Bob Sharp with respect to

professional reputation.


would be a good PhD

advisor for Bob Sharp with

respect to reputation

among peers.


would be a good PhD advisor

for Bob Sharp with respect to

quality of student results.

Assess whether John

Doe would be a good

PhD advisor for Bob

Sharp with respect to

research funding.

Common area

of interest, etc.

Logic to assess John Doe based on elementary criterion.

professional reputation,

learning experience, … ,

quality of student results

Main PhD advisor criteria

Sub-criteria of professional reputation

… Logic to assess John Doe based on elementary criterion.

…

…

…

It is very likely that John Doe would

be a good PhD advisor for Bob

Sharp in Artificial Intelligence.

It is very likely that John Doe would be

a good PhD advisor for Bob Sharp.

It is almost

certain that

John Doe

would be ...

It is almost certain that

John Doe would be a good

PhD advisor for Bob Sharp

with respect to reputation

among peers.

It is very

likely that

John Doe

would be ...

It is very likely that

John Doe would be a

good PhD advisor for

Bob Sharp with respect

to research funding.

…

2010 Gheorghe Tecuci, Learning Agents Center 14

Hybrid Knowledge Base = Ontology + Rules

PhD advisor

Jane Austin John Doe

faculty member staff member

professor

university employee

instance of instance of

subconcept of

subconcept of

instructor

full professor

associate professor

assistant professor

instance of

subconcept of

John Smith

Ontology:

Hierarchical

representation of the

domain concepts

IF the problem to solve is P1g

THEN solve its sub-problems

P1 … P1

Condition

Except-When Condition

1g ng

… Except-When Condition

Problem reduction and

solution synthesis rules:

Specified with the concepts

from the ontology



P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng


S1

S11 S1

n

S21 S2

m

S31 S3

p

QuestionAnswer

QuestionAnswer

QuestionAnswer

P1

P11

P1n

P21 P2

m

P3pP3

1

QuestionAnswer

QuestionAnswer

QuestionAnswer

Reasoning tree:

Finding the

solution of a

specific problem

2010, Learning Agents Center Jane Austin

Ph.D. student

John Doe

faculty member staff member

professor

studentuniversity employee

person

Bob Sharp

instance of

subconcept of


subconcept of

subconcept ofsubconcept of

subconcept of

M.S. student

B.S. studentinstructor

graduatestudent undergraduate

student

fullprofessor

associateprofessor

assistantprofessor

subconcept of

instance of

subconcept of

Joan Dean

instance of

PhDadvisor

John Smith

graduateresearchassistant

teachingassistant

employee

subconcept of

subconcept of

Learned with an

evolving ontology

15

Partially Learned Rules and Evolving Ontology

IF the problem to solve is P1


P1 … P1

PVS Condition

Except-When

PVS Condition

1 n

S1

S11 S1

n

S21 S2

m

S31 S3

p

QuestionAnswer

QuestionAnswer

QuestionAnswer

P1

P11

P1n

P21 P2

m

P3pP3

1

QuestionAnswer

QuestionAnswer

QuestionAnswer

Partially

learned

rules with

plausible

version

space

(PVS)

conditions

+

-

PVS Condition Except-When

PVS Condition

2010, Learning Agents Center

Agent Development and Maintenance Tasks

Specify

instances

& features

Learn

ontological

elements

Import and

develop initial

ontology

Learn

reasoning

rules

Provide and

explain

examples

Analyze

agent’s

solution

Refine

rules

Explain

errors

Develop

reasoning

trees

Instruct

expert to

use model

Teaching and Learning

Claim: Much easier and faster because many tasks are performed by the learning agent (cognitive assistant) itself.

Subject Matter Expert

Knowledge Engineer

Learning Agent

Develop

ontology

Define

reasoning

rules

Verify and

update rules

Programming Very difficult and time-consuming

Model

problem

solving

Subject Matter Expert

Knowledge Engineer

S1

S11 S1

n

S21 S2

m

S31 S3

p

QuestionAnswer

QuestionAnswer

QuestionAnswer

P1

P11

P1n

P21 P2

m

P3pP3

1

QuestionAnswer

QuestionAnswer

QuestionAnswer

PhDadvisor

Jane AustinJohn Doe

faculty memberstaff member

professor

university employee


subconcept of

subconcept of

instructor

fullprofessor

associateprofessor

assistantprofessor

instance of

subconcept of

John Smith



P1 … P1

Condition


1g ng

…Except-When Condition



P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng




P1 … P1

Condition


1g ng


Reasoning Tree

Mixed-Initiative Problem Solving

Accept Reasoning Step

Reject Reasoning Steps

Rules Refinement

Problem

Extend Reasoning Tree

Explain Examples

Rules Learning

Explain Examples

Explain Examples

Refined Rules

Refined Ontology

Learned Rules

Modeling, Learning, and Problem Solving

Ontology + Rules S3

1 S3p

S11

S21

QuestionAnswer

S2m

QuestionAnswer

P1

P11

P1n

QuestionAnswer

P21 P2

m

QuestionAnswer

P3pP3

1

QuestionAnswer


The subject matter expert helps the agent to learn (e.g. by

providing examples and explanations), and the agent helps

the expert to teach it (e.g. by asking relevant questions).

Integrated Teaching and Learning

Input knowledge

Problem solving behavior

Explicit learning guidance

Explicit teaching guidance

Hints and answers

to agent’s questions

Problem solving examples,

Explanations

Attempted solutions

to problems

Questions


P1

P1 1

P1 n

Question Q Answer A

…

Example

Rule Learning Method

Knowledge Base

1. Ontology-based

mixed-initiative

understanding

f1

ob1 ob2

ob3

f2

Explanation

Problem P1g

condition

Knowledge Base

3. Minimal and maximal

ontology-based generalizations

guided by analogical reasoning



P1 … P1 1g ng

Qg Ag

Applicability condition

Problem Png

condition

1

Problem P1g

condition

1 …

2. Example and

explanation

reformulation



P1 … P1 1g ng

Qg Ag

?O1 is ob1

f1 ?O3

?O2 is ob2

f2 ?O3

?O3 is ob3


The expert makes explicit how to solve a problem

1. Modeling

Modeling and Learning

The agent learns reduction rules

2. Learning

Rule1

Rule2


Rule Learning

Problem Reduction

Example

Problem Rule

Problem Rule

Reduction Rule


Rule Learning

?O1 is John Doeis expert in ?O3

?O2 is Bob Sharpis interested in ?O3

?O3 is Artificial Intelligence

Condition:

1. Ontology-based

mixed-initiative

understanding

Explanation

2. Example and

explanation

reformulation

Knowledge Base

Bob Sharp

Artificial Intelligence

is expert in is interested in

John Doe


23

Minimal and Maximal Generalizations

John Doe

PhD advisor

Bob Sharp


PhD student

employee

subconcept of

?O2 ?O1

person

associate

professor

subconcept of

graduate

student

student

subconcept of

professor

faculty member

university employee

actor

object

?O1 is John Doe

is expert in ?O3

?O2 is Bob Sharp

is interested in ?O3

?O3 is Artificial Intelligence

Most s

pecific

genera

lizatio

n

Most g

enera

l

genera

lizatio

n

LB

LB

LB

UB

Artificial

Intelligence

PhD

research

area

Computer

Science

research area

LB

UB

instance of

?O3 feature

is expert in domain

range

person

subconcept-of

is interested in domain

range

subconcept-of

research area

Object

ontology

Feature

ontology


Solving, Critiquing, and Refinement

Applies learned rules to solve new problems 1. Solving

Correct

2. Critiquing

Refines rule

with positive

example

3. Refinement


Failure

explanation Examples of problem

reductions generated by agent

Incorrect

example

Learning from

Explanations

Learning by Analogy

and Experimentation

Learning from Examples

Knowledge Base

Refinement Strategy: PVS Condition Refinement

_

+ +

Tom Mitchell

Version

spaces: A

candidate

elimination

approach to

rule learning

IJCAI 1977

Optional

Explanation

Correct

example


Generalization with a Positive Example

?O1 is Dan Smith is expert in ?O3 ?O2 is Bob Sharp is interested in ?O3

?O3 is Information Security

Positive example

Rule’s main condition

Dan Smith

PhD advisor

instance of

full

professor

subconcept of

professor

associate

professor

Refined Rule




Correct

Incorrect because

Dan Smith plans to

retire

2. Critiquing

Refines rule

with positive

example

3. Refinement

Refines

rule with

negative

example


Failure

explanation Example of problem reductions

generated by the agent

Incorrect

example

Correct

example

Learning from

Explanations

Learning by Analogy

and Experimentation

Learning from Examples

Knowledge Base

IF we have to solve

<Problem>

THEN solve

<Sub-problem 1> … <Sub-problem m>

Main

PVS Condition

Except-When

PVS Condition

Refinement Strategy: Except-When PVS Condition


Rewrite as

Except When Condition 1

?O1 is Dan Smith

plans to retire from ?O4

?O4 is George Mason University

Failure Explanation

Dan Smith plans to retire from

George Mason University

Negative Example

Rule Specialization with a Negative Example Rule




Refines

rule with

positive

example

3. Refinement

Refines

rule with

negative

example

Correct

2. Critiquing

Incorrect because

Jane Austin

plans to move


Rewrite as

Except When Condition 2

?O1 is Jane Austin

plans to move to ?O4

?O6 is Indiana University

Failure Explanation

Jane Austin plans to move to

Indiana University

Rule Specialization

Negative Example


Applies

learned

rules to

solve new

problems

1. Solving

Solving, Modeling, and Learning

2. Modeling

Extends the

reasoning tree

Learns a

new rule

3. Learning

Rule


Rule Learning

REDUCTION EXAMPLE

LEARNED

REDUCTION RULE



34


Refines

rule with

negative

example

3. Refinement

Rule

Correct

2. Critiquing

Incorrect because of the “even chance” likelihood


Refined Rule

Incorrect reduction

Negative Example


Problem Solving

Modeling

Learning

Refined rule

Expert example

Rule-based guidance

Creative solution

Context for creative solution Generated

example Mixed

Initiative

Reasoning

Synergy of Modeling, Learning, and Problem Solving

AcceptReasoning Steps

RejectReasoning Steps

Rule Refinement

ExtendReasoning Tree

ExplainExamples

Rule Learning

ExplainExamples

ExplainExamples

Refined Rules

Refined Ontology

Learned Rules

Reasoning Tree

Problem

Mixed-Initiative Problem Solving

Ontology + Rules

S1

S11 S1n

S111 S11mP11mP111

P1nP11

P1

…

…

Modeling, learning, and

problem solving mutually

support each other to

capture tacit knowledge


Characterization of the Disciple Learning Method

• Uses a form of multistrategy learning that synergistically integrates learning from

examples, learning from explanations, and learning by analogy.

• Uses the explanation of the first example and analogical reasoning to generate a

much smaller version space than Mitchell’s classical version space method.

• Efficiently searches the version space, guided by explanations obtained through

mixed-initiative reasoning with the user (both the upper bounds and the lower

bounds are both generalized and specialized to converge toward one another).

• Learns from only a few examples, in the context of an incomplete and evolving

ontology.

• Learns even in the presence of exceptions.

• Keeps minimally generalized examples and

explanations to automatically regenerate the

rules when the ontology changes.

• Efficiently captures expert’s tacit knowledge,

significantly reducing the complexity of developing cognitive assistants.

• Applied to many complex real-world domains (intelligence analysis, military strategy

and planning, education, collaborative emergency response, etc.)

+

-


Overview

Final Remarks






Intelligence Analysis

• The goal of Intelligence Analysis

is to answer complex questions for

decision-making, such as:

Does Al Qaeda have nuclear weapons?

Will the United States be the world leader in

alternative fuels within the next decade?

• Complex arguments, requiring both

imaginative and critical reasoning, are

necessary in order to establish and

defend the relevance, the believability,

and the inferential force of evidence,

with respect to the questions asked.

• The answers are necessarily

probabilistic in nature because our

evidence is always incomplete, usually

inconclusive, frequently ambiguous,

commonly dissonant, and with various

degrees of believability.

David A.

Schum

The Evidential

Foundations

of Probabilistic

Reasoning,

Northwestern

University

Press,

1994, 2001.

John H.

Wigmore

The Science of

Judicial Proof:

As Given by Logic,

Psychology, and

General Experience

and Illustrated in

Judicial Trials,

Little,Brown&Co,

Boston, 1937


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 of

intelligence analysis

expertise 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.

Disciple-LTA: Analyst’s Cognitive Assistant


Sample Problem: Analysis of Wide-Area Motion Imagery

From: Mita Desai, Multi-entity activity discovery over large

space-time windows, DARPA,

http://www.darpa.mil/ipto/solicit/baa/BAA-09-55_ID01.pdf

Real‐Time Analysis

Compare tracks against

known movement

patterns, or sets and

sequences of events,

and find matches that

may indicate an

impending threat event

(e.g., an ambush).

Forensic Analysis

Backtrack from a threat

event (e.g., ambush,

rocket launch) and

discover participants,

possible related

locations and events,

and movement patterns.


Discovery of Evidence, Hypotheses and Arguments

Evidence in search of hypotheses

What threat does this evidence suggest?

E*: Evidence of road work

at Al Batha highway junction

at 1:17AM

Not Road work

Road repair

Traffic disruption

Potential Items of

Evidence

Abductive reasoning

Hk: Ambush threat at the Al Batha highway junction at 1:17AM

P Possibly Q

Evidential tests of hypotheses

What is the likelihood of the threat based on the available evidence?

Items of Evidence

Inductive reasoning

Hk: Ambush threat very likely

P Probably Q

Hypotheses in search of evidence

Assuming that the threat is real, what other events or entities should be observable?

Hk: Ambush threat

Deductive reasoning

P Necessarily Q

Hc: Ambush preparation

Hi: Ambush location

Ha: Road blocking

E: Road work



Mixed-Initiative Multistrategy Learning

E*: Evidence of road work

at Al Batha highway junction

at 1:17AM

Not Road work

Road repair

Traffic disruption

Potential Items of

Evidence

Abductive reasoning

Hk: Ambush threat at the Al Batha highway junction at1:17AM

P Possibly Q

Items of Evidence

Inductive reasoning

Hk: Ambush threat very likely

P Probably Q

Hk: Ambush threat

Deductive reasoning

P Necessarily Q


Hi: Ambush location

Ha: Road blocking

E: Road work


Abd Rule Ded Rule Ind Rule


Wigmorean Network for Hypothesis Analysis

Assess H1

Assess the favoring

evidence for H12

Assess the disfavoring

evidence for H12

Assess the relevance of

E1 to H12

Assess the believability

of E1

Assess the extent to which E1 favors H12


Assess H11

Assess H12

Assess H13

E*i

Relevance answers the question:

So what? How does this item of

information bear on what we are

trying to prove or disprove?

If we believe E1 then H12 is almost certain



Assess H1

Assess the favoring

evidence for H12


evidence for H12

It is likely that E1 is

true



E1 to H12


of E1



Assess H11

Assess H12

Assess H13

E*i

Believability answers the question:

Can we believe what this item of

intelligence information is telling us?



Assess H1

Assess the favoring

evidence for H12


evidence for H12

Based on E1 it is likely that H12 is true

Inferential force of E1 on H12

Based on the favoring evidence it is almost

certain that H12 is true

Inferential force of favoring evidence on H12

It is very likely that H12 is true

Inferential force of evidence on H12

It is likely that E1 is

true



E1 to H12


of E1

Based on E2 it is almost certain that H12 is true



Based on the disfavoring evidence it is an even

chance that H12 is false

Assess H11

Assess H12

Assess H13

It is almost certain that H11 is true


E*i

Inferential Force or Weight

answers the question:

How strong is this item of relevant

evidence in favoring or disfavoring various

alternative hypotheses being entertained?


Inferential force of evidence on H1


Believability of Evidence

Evidence ontology

evidence

tangible evidence

testimonial evidence

demonstrative tangible evidence

real tangible evidence

unequivocal testimonial evidence

equivocal testimonial evidence


based upon direct

observation

authoritative record

missing evidence


obtained at second hand

testimonial evidence based on opinion

completely equivocal testimonial evidence

probabilistically equivocal testimonial evidence

believability of E

authenticity of E

reliability of E

accuracy of E

Believability assessments

believability of E

Source’s competence

Source’s credibility

Source’s understandability

Source’s access

Source’s veracity

Source’s objectivity

Source’s observational

sensitivity


S1

S11 S1n

S111 S11m P11m P111

P1n P11

P1

…

…

Sa11m Sd

11m Pd11m Pa

11m …

S1 P1

Multi-Agent and Multi-Domain Problem Solving

S11m P11m

S11 P11

S1n P1n

The problem reduction / solution synthesis paradigm facilitates:

collaboration between users assisted by their agents;

solving problems requiring multi-domain expertise.


The Arch of Knowledge

Evidential tests of hypotheses

Hypotheses in Search of evidence

New Observations Evidence in search

of hypotheses

Hypothesis

Observations

Likelihood of

Hypothesis

Galileo

He

Hc

Ha

Ei

E*

Items of

Evidence

Inductivereasoning

Hk: It is likely that a dirty bomb will be set off inthe Washington DC area.

Hd

Hk true

He

DeductivereasoningHd

Potential

Items of

Evidence

Hc

Ha

Ei

E*

Abductivereasoning

Hk: A dirty bomb will be set off in the Washington

DC area.

E*: Report on cesium-137

canister missing

Ha: stolen

E: missing

Hc: stolen by terrorist

organization

He: build dirty bomb

not missing

lostused inproject

stolen by competitor

stolen by employee

Aristotle Newton Locke

Herschel Whewell Peirce

Oldroyd Schum


The Arch of Knowledge Everywhere

New Observable Phenomena

Possible Hypotheses or Explanations

Observations of Events in Nature

New or Revised Theory

Intelligence

Analysis

Science

New Potential Evidence

Possible Charges or Complaints

Observations during Fact Investigation

Verdict

Law

New Potential Evidence

Possible Hypotheses

Observations of Events in the World

Likelihood of Hypotheses


Overview

Final Remarks






Future Research Directions

• Natural language understanding and generation

• Abductive reasoning and learning

• Abstraction reasoning and learning

• Multistrategy reasoning and learning

• Integration of logic with different probability systems (Fuzzy,

Baconian, Naïve Bayes, Dempster-Shapher)

• Modeling evidence-based decision making

• Scaling-up the developed methods and tools

• Application in a variety of domains (education, intelligence,

defense, medicine, etc.)


Research Vision for the Disciple Learning Assistants

Mainframe

Computers

Personal

Computers

Learning

Assistants


Questions


This research was performed in the Learning Agents Center and was

supported by George Mason University and by several agencies of

the U.S. Government, including the Department of Defense, the

National Geospatial-Intelligence Agency, the Intelligence Community,

the Air Force Office of Scientific Research, the Air Force Research

Laboratory, the Defense Advanced Research Projects Agency, the

National Science Foundation, the U.S. Army War College, and the

Joint Forces Staff College. The U.S. Government is authorized to

reproduce and distribute reprints for Government purposes

notwithstanding any copyright notation thereon.

Acknowledgements and Contact Information

Contact information: Dr. Gheorghe Tecuci

Professor of Computer Science and Director of the Learning Agents Center

MSN 6B3, Learning Agents Center, George Mason Univ., Fairfax, VA 22030

[email protected] tel: 703 993 1722 http://lac.gmu.edu/

gheorghe tecuci mihai boicu, dorin marcu, david schum, kathryn … · invited talk the 9th int....

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