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From Personal Computers to Learning Assistants. Gheorghe Tecuci Learning Agents Center and Computer Science Department School of Information Technology and Engineering George Mason University. 21 September 2005. Overview. Learning Agents Center: Research Vision.

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From Personal Computers

to Learning Assistants

Gheorghe Tecuci

Learning Agents Center

and Computer Science Department

School of Information Technology and Engineering

George Mason University

21 September 2005


Overview

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


http://lac.gmu.edu

Mission

  • Conducts fundamental and experimental research on the development of knowledge-based learning and problem solving agents.

  • Supports teaching in the areas of intelligent agents, machine learning, knowledge acquisition, artificial intelligence and its applications.

  • Develops the Disciple theory, methodology and agent shells for building agents that can be taught how to solve problems by subject matter experts.

Basic Research

Tools

Applications

Transitions


Teaching as Alternative to Programming

Building an intelligent machine by programming is too difficult.

“Instead of trying to produce a programme 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.”

Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.


How are Expert Systems Built and Why it is Hard

Edward Feigenbaum, 1993:Rarely does a technology arise that offers such a wide range of important benefits.



Disciple’s Vision on the Future of Software Development

Learning

Agents

Personal

Computers

Mainframe

Computers


Vision on the Use of Disciple in Education

teaches

teaches

teaches

DiscipleAgent

DiscipleAgent

DiscipleAgent

DiscipleAgent

KB

KB

KB

KB

teaches

 2005, Learning Agents Center


Overview

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


The Overall Architecture of a Disciple Agent

 2005, Learning Agents Center


Knowledge Base = Ontology + Rules

PROBLEM SOLVING TASK

ONTOLOGY FRAGMENT

Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence.

IF: Determine whether ?O1 can be a PhD

advisor for ?O2 in ?O3.

Main condition

?O1 is PhD_advisor

has_as_employer ?O4

has_as_position ?O5

?O2 is PhD_student

?O3 is research_area

?O4 is university

?O5 is tenured_position

Except when condition

?O1 is person

is_likely_to_move_to ?O6

?O6 is employer

THEN: Determine whether ?O1 would

be a good PhD advisor for ?O2 in ?O3.

REASONING RULE


Main Idea of the Disciple Approach

With Disciple

Instruct SME to explain reasoning

Import and

develop initial

ontology

Define and

explain

examples

Critique

examples

SME

SME

KE

SME

Agent

Agent

SME

KE

Develop reasoning trees

Specify

instances and features

Learn

ontological

elements

Learnreasoning

rules

Explain

critiques

Refine

rules

SME

Agent

SME

Agent

Agent

SME

Agent

Agent

KE

Model the reasoning of SME

Create

object ontology

Define

reasoning rules

Verify and

update rules

SME

Traditionally

Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence.


Research Issues for Learning Agents

Problem Solving Paradigm for Expert-Agent Collaboration

Learning with an Evolving Representation Language

Plausible Reasoning with Partially Learned Knowledge

Integrated Teaching and Learning

Multistrategy Learning

Agent Architecture for Generality-Power Tradeoff

Knowledge Base Structuring for Knowledge Reuse


Problem solving paradigm for expert agent collaboration
Problem Solving Paradigm for Expert-Agent Collaboration

Task reduction and solution composition

guided by questions and answers

T1

S1

Q1

S11

A1n

A11

S1n

T1n

T11a

S11a

T11b

S11b

Q11b

S11b

S11bm

S11b1

A11bm

A11b1

T11b1

T11bm



Plausible reasoning with partially learned knowledge
Plausible Reasoning with Partially Learned Knowledge

IF

<task>

Plausible Upper Bound Condition<PUB condition>

Plausible Lower Bound Condition<PLB condition>

THEN

<subtask 1>

<subtask m>


Mixed initiative problem solving
Mixed-Initiative Problem Solving

Problem

Creative solutions

Inventive solutions

Innovative solutions

Routine solutions

Solution


Integrated teaching and learning
Integrated Teaching and Learning

examples,

facts,

rules

Input knowledge

learning hints

Explicit learning guidance

classification of examples,

problem solutions

Problem solving behavior

Explicit teaching guidance

questions


Rule Learning Method

Analogy and Hint

Guided Explanation

Analogy-based

Generalization

Plausible version space rule

plausible explanations

PUB

guidance, hints

Example of a

task reduction

step

PLB

Incomplete

explanation

analogy

Knowledge Base



Overview

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


Challenges for the Intelligence Analyst

P

A

H1

Hn

Knowledge

Difficult to share intelligence

Difficult to collaborate with other analysts and experts

Overwhelmed by information

Intelligence analysis is very difficult

Difficult to consider multiple hypotheses

Difficult to train new analysts

Difficult to rigurously explain the analysis

Difficult to find time for critical analysis and AARs

Difficult to analyze in reference to the culture of the data source

Difficult to acquire and retain expertise

Difficult to avoid the analytic mindset


Investigated Solution

An integrated approach to intelligence analysis research, education, and operations.

  • Develop a new type of intelligent agent that

    • can rapidly acquire expertise in intelligence analysis,

    • can train new intelligence analysts, and

    • can assist the analysts to solve complex problems.


Vision: Integration of Research, Education, and Operations

Building an agent shell

DISCIPLE-LTA

1

Rapid agent development

Agent optimization

Knowledge baseoptimization

and re-use

Agent training

by expert analyst

DISCIPLE-LTA

2

6

DISCIPLE-LTA

Expert analyst and knowledge engineer

Knowledge engineer and expert analyst

Teaching new analysts

After action review andagent personalization

3

5

Intelligent tutoring

DISCIPLE-LTA

DISCIPLE-LTA

Analyst

Analyst

4

Analyst’s assistant

(mixed-initiative learning)

Analyst’s assistant

(mixed-initiative analysis)

Agent use andnon-disruptive learning

DISCIPLE-LTA

Analyst

Knowledge

engineer

Agent

Lifecycle


Vision: Use of Disciple-LTA Agents in an Operational Environment

Disciple Client

Disciple-LTA

GLOBAL

KNOWLEDGE BASE

SEARCH ENGINES

Libraries

Knowledge Repositories

Massive Databases

Disciple-LTA

Intelligent

agent

Disciple-LTA


Synergistic Integration of Research and Education Environment

Develop a systematic approach to military intelligence analysis

Experimentation with Disciple-LTA in the 589 MAAI elective

Military

Education& Practice

Military

Research

Working closely with the expert analysts

in a multi-disciplinary research

Working closely with the end user to receive crucial and timely feedback

DiscipleLTA

IntelligentAgents

Research

Agent development by expert analysts using learning agent technology

 2005, Learning Agents Center


US Army War College Course Environment

589 Military Applications of Artificial Intelligence: Intelligence Analysis

Live Experiment



Assess whether Location-A is a training base for terrorist operations

What type of factors should be considered to assess the presence of a terrorist training base?


What type of factors should be considered to assess the presence of a terrorist training base?

Political environment, physical structures, flow of suspected terrorists, weapons and weapons technology, other suspected bases in the region, and terrorist sympathetic population


Political environment, physical structures, flow of suspected terrorists, weapons and weapons technology, other suspected bases in the region, and terrorist sympathetic population

Assess whether there is a flow of suspected terrorists in the region of Location-A

Assess whether there are other suspected bases for terrorist operations in the region of Location-A

Assess whether the political environment would support a training base for terrorist operations at Location-A

Assess whether there is terrorist sympathetic population in the region of Location-A

Assess whether the physical structures at Location-A support the existence of a training base for terrorist operations

Assess whether there are weapons and weapons technology at Location-A that suggest the presence of a training base for terrorist operations


Assess whether there is a flow of suspected terrorists in the region of Location-A

Assess whether there are other suspected bases for terrorist operations in the region of Location-A

Assess whether the political environment would support a training base for terrorist operations at Location-A

Assess whether there is terrorist sympathetic population in the region of Location-A

Assess whether the physical structures at Location-A support the existence of a training base for terrorist operations

Assess whether there are weapons and weapons technology at Location-A that suggest the presence of a training base for terrorist operations


Intelligence Experts Opinion: Quotations the region of Location-A

REVIEWER #1: a grand challenge to develop an intelligent agent capable of learning, tutoring and decision support …

if implemented it would likely be pretty unique.

REVIEWER #2: This is an innovative idea that could revolutionize the way we do business, enable us to be more efficient, more effective, more thorough.

REVIEWER #3: a very important R&D area for next generation intelligence analysis. The work is well founded, and the execution of real software to implement the ideas is substantial.

REVIEWER #4: I have seen a briefing on the work presented here last year and was impressed with the initial ease of use of capturing complex concepts. This could be excellent for use in both training analysts as well as capturing knowledge from more senior analysts.


Overview the region of Location-A

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


DARPA’s Rapid Knowledge Formation Program the region of Location-A

Develop the Disciple technology to enable teams of subject matter experts to build integrated knowledge bases and agents incorporating their problem solving expertise.

Disciple-RKF

Assistant

Problem solver for a

non-expert

KB1

...

Disciple-RKF

Assistant

Expert

Assistant of an expert

Integrated KB

Disciple-RKF

Assistant

Tutor

to a student

KBn

Expert

Successful experiments and transition to the US Army War College


Center of Gravity Analysis the region of Location-A

The center of gravity of an entity is its primary source of moral or physical strength, power or resistance.

Joe Strange,

Centers of Gravity & Critical Vulnerabilities,

Marine Corps War College, 1996.

If a combatant eliminates or influences the enemy’s strategic center of gravity, then the enemy will lose control of its power and resources and will eventually fall to defeat. If the combatant fails to adequately protect his own strategic center of gravity, he invites disaster.

P.K. Giles and T.P. Galvin

US Army War College, 1996.


Use of Disciple at the US Army War College the region of Location-A

589jw Military Applications of Artificial Intelligence

Students teach Disciple their COG analysis expertise, using sample scenarios(e.g. Iraq 2003, War on terror 2003, Arab-Israeli 1973)

Students test the trained Disciple agent based on a new scenario (North Korea 2003)

Global evaluations of Disciple by officers during three experiments

I think that a subject matter expert can use Disciple to build an agent, with limited assistance from a knowledge engineer

Spring 2001

COG identification

Spring 2002

COG identification

and testing

Spring 2003

COG testing based on critical capabilities


Use of Disciple at the US Army War College the region of Location-A

319jw Case Studies in Center of Gravity Analysis

Disciple helps the students to perform a center of gravity analysis of an assigned war scenario.

Disciple was taught based on the expertise of Prof. Comello in center of gravity analysis.

Problemsolving

Teaching

DiscipleAgent

KB

Learning

Global evaluations of Disciple by officers from the Spring 05 course

Disciple helped me to learn to perform a strategic COG analysis of a scenario

The use of Disciple is an assignment that is well suited to the course's learning objectives

Disciple should be used in future versions of this course


Parallel development and merging of KBs the region of Location-A

432 concepts and features, 29 tasks, 18 rules

For COG identification for leaders

Initial KB

Domain analysis and ontology development (KE+SME)

Knowledge Engineer (KE)

All subject matter experts (SME)

Training scenarios:

Iraq 2003

Arab-Israeli 1973

War on Terror 2003

Parallel KB development (SME assisted by KE)

37 acquired concepts and

features for COG testing

Extended KB

DISCIPLE-COG

DISCIPLE-COG

DISCIPLE-COG

DISCIPLE-COG

DISCIPLE-COG

stay informed

be irreplaceable

communicate

be influential

have support

be protected

be driving force

Team 1

Team 2

Team 3

Team 4

Team 5

5 features

10 tasks

10 rules

14 tasks

14 rules

2 features

19 tasks

19 rules

35 tasks

33 rules

3 features

24 tasks

23 rules

KB merging (KE)

Learned features, tasks, rules

Integrated KB

Unified 2 features

Deleted 4 rules

Refined 12 rules

Final KB:

+9 features  478 concepts and features

+105 tasks 134 tasks

+95 rules 113 rules

5h 28min average training time / team

3.53 average rule learning rate / team

COG identification and testing (leaders)

DISCIPLE-COG

Testing scenario:

North Korea 2003

Correctness = 98.15%


Current Project the region of Location-A

Distributed Knowledge Acquisition, Validation, and Maintenance

PROBLEM SOLVING

AND LEARNING

ASSISTANT

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Operational Use and Non-Disruptive Learning

After Action Review and KB Refinement

Knowledge acquired by the agents is validated and integrated into an improved Disciple Knowledge Base

Integration Team: Knowledge engineer +

Subject matter experts

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Operational Use and Non-Disruptive Learning

KB

INTEGRATION

ASSISTANT

KB Integration, Validation and Maintenance

PROBLEM SOLVING

AND LEARNING

ASSISTANT

After Action Review and KB Refinement

Copies of Disciple agents support users’ decision-making and all learn from these experiences.

PROBLEM SOLVING

AND LEARNING

ASSISTANT

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Operational Use and Non-Disruptive Learning

After Action Review and KB Refinement


Army War College the region of Location-A

Co-PI, SME

Dr. Jerome Comello

Experiments in

2005, 2006, 2007

PROBLEM SOLVING

AND LEARNING

ASSISTANT

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Operational Use and Non-Disruptive Learning

After Action Review and KB Refinement

Integration Team: Knowledge engineer +

Subject matter experts

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Experimentation Environment

2005, 2006, 2007

Operational Use and Non-Disruptive Learning

KB

INTEGRATION

ASSISTANT

KB Integration, Validation and Maintenance

PROBLEM SOLVING

AND LEARNING

ASSISTANT

George Mason

University

After Action Review and KB Refinement

Air War College

Co-PI, SME

Col Jeffrey Hightaian

LtCol Todd Kemper

Experiments in

2006, 2007

Marine Corps War College

Co-PI, SME

Dr. Joseph Strange

Experiments in

2006, 2007

PROBLEM SOLVING

AND LEARNING

ASSISTANT

PROBLEM SOLVING

AND LEARNING

ASSISTANT

Operational Use and Non-Disruptive Learning

After Action Review and KB Refinement


Overview the region of Location-A

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


Virtual Experts for Multi-Domain Collaborative Planning the region of Location-A

Sample scenario: Planning the response to an emergency situation involving a tanker truck

leaking red-fuming nitric acid near a student residential area of GMU.

User’s Assistant

Scenario

Specification

Report Generator

Virtual Experts (VE) Library

Profile-based Team Selector

User

Plan

Browser

Knowledge Management

Local Knowledge Base

Disciple-VE

Disciple-VE

Disciple-VE

Disciple-VE

Disciple-VE

Disciple-VE

Assistant Training

Modules

Plan

Abstraction

Ontology

Rules

Disciple-VE

Disciple-VE

Disciple-VE

Virtual Team Manager

Disciple-VE

Disciple-VE

Team of Virtual Experts

DISTRIBUTED

KNOWLEDGE BASE

Disciple-VE

CollaborativePlanner

KB

IndicatorsIdentification

Plan

Brainstorming

Disciple-VE

Knowledge Management

KB

KB

VE Training

Modules

Local Knowledge Base

External-

Expertise

Agent

KB

KB

KB

Ontology

Rules

Plan

Grading

Assumption-based

Reasoning

Disciple-VE

Disciple-VE


Overview the region of Location-A

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


DARPA’s HPKB Challenge Problem the region of Location-A

Rapid development and evaluation of a Course of Action critiquer

To what extent does this course of action conform to the principle of surprise?


DARPA’s HPKB Program: Evaluation the region of Location-A

100%

  • High knowledge acquisition rate;

  • Better results than the other evaluated systems;

  • Better performance than the evaluating experts (many unanticipated solutions).


Overview the region of Location-A

Learning Agents Center: Research Vision

Research Issues for Learning Agents

Personal Cognitive Assistant for Intelligence Analysis

Agents for Centers of Gravity and Critical Vulnerabilities

Virtual Experts for Multi-domain Collaborative Planning

Agent for Course of Action Critiquing

Final Remarks


Disciple’s Vision on the Future of Software Development the region of Location-A

Personal

Computers

Learning

Agents

Software systems developed and used by persons that are not computer experts

Software systems developed

by computer experts

and used by persons that

are not computer experts

Mainframe

Computers

Software systems developed and used by computer experts


Vision on the Use of Disciple in Education the region of Location-A

teaches

teaches

teaches

teaches

The expert/teacher teaches Disciple through examples and explanations, in a way that is similar to how the expert would teach a student.

DiscipleAgent

DiscipleAgent

DiscipleAgent

DiscipleAgent

KB

KB

KB

KB

Disciple tutors the student in a way that is similar to how the expert/teacher has taught it.

 2005, Learning Agents Center


Acknowledgements the region of Location-A

The research performed in the Learning Agents Center was sponsored by several US government agencies including Defense Advanced Research Projects Agency, Air Force Office of Scientific Research, Air Force Research Laboratory, National Science Foundation, and Army War College.


Questions the region of Location-A


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