Learning Agents Center

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Development and Use of Intelligent
Decision-making Assistants The Disciple Approach
Gheorghe Tecuci, Mihai Boicu, Dorin Marcu, Bogdan
Stanescu, Cristina Boicu, Marcel Barbulescu
Learning Agents Center George Mason University
Computer Science Department Partners Day
Symposium May 4, 2004
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Overview
Research Problem, Approach, and Application
Knowledge Representation, Reasoning, and Learning
Experiments of Agent Development and Use
Long Term Research Vision
Acknowledgements
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How are agents built and why it is hard
Intelligent Agent
Knowledge
Domain
Inference Engine
Engineer
Expert
Dialog
Programming
Knowledge Base
Results
The knowledge engineer attempts to understand how
the subject matter expert reasons and solves
problems and then encodes the acquired expertise
into the agent's knowledge base. This modeling
and representation of experts knowledge is long,
painful and inefficient (known as the knowledge
acquisition bottleneck).
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Research Problem and Approach
Research Problem Elaborate a theory, methodology
and family of systems for the development of
knowledge-base agents by subject matter experts,
with limited assistance from knowledge engineers.
Approach Develop a learning agent that can be
taught directly by a subject matter expert while
solving problems in cooperation.
The expert teaches the agent how to
perform various tasks in a way that resembles how
the expert would teach a person.
The agent learns from the expert, building,
verifying and improving its knowledge base
1. Mixed-initiative problem solving 2.
Teaching and learning 3. Multistrategy
learning
Problem Solving
Ontology Rules
Interface
Learning
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Synergistic collaboration and transition to the
USAWC
George Mason University - US Army War College
Students developed scenarios
319jw Case Studies inCenter of Gravity Analysis
Students developed agents
589jw Military Applications of Artificial
Intelligence
Use of Disciple in a sequence of two joint
warfighting courses
Military Education Practice
Military Strategy Research
Disciple
Formalization ofthe Center of Gravity(COG)
analysis process
ArtificialIntelligence Research
Knowledge bases and agent development by subject
matter experts, using learning agent technology.
Experiments in the USAWC courses.
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Sample Domain Center of Gravity Analysis
Centers of Gravity Primary sources of moral or
physical strength, power or resistance of the
opposing forces in a conflict.
Application to current war scenarios (e.g. War on
terror, Iraq) with state and non-state actors
(e.g. Al Qaeda).
Identify COG candidates
Test COG candidates
Identify potential primary sources of moral or
physical strength, power and resistance from
Test each identified COG candidate to determine
whether it has all the necessary critical
capabilities
Which are the critical capabilities? Are the
critical requirements of these capabilities
satisfied? If not, eliminate the candidate. If
yes, do these capabilities have any vulnerability?
Government Military People Economy Alliances Etc.
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Problem Solving Approach Task Reduction

• A complex problem solving task is performed by
• successively reducing it to simpler tasks
• finding the solutions of the simplest tasks
• successively composing these solutions until
  the solution to the initial task is obtained.

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Problem Solving and Learning
We need to
Identify and test a strategic COG
candidatecorresponding to a member of the
Allied_Forces_1943
Which is a member of Allied_Forces_1943?
US_1943
EXAMPLE OF REASONING STEP
Therefore we need to
ONTOLOGY FRAGMENT
Identify and test a strategic COG candidate for
US_1943
LEARNED RULE
IF Identify and test a strategic COG candidate
corresponding to a member of a force The force
is ?O1

IF Identify and test a strategic COG candidate
corresponding to a member of the ?O1

FORMAL STRUCTURE
Plausible Upper Bound Condition
?O1 is multi_member_force has_as_member ?O2
?O2 is force
Question Which is a member of ?O1 ? Answer
?O2
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance has_as
_member ?O2 ?O2 is single_state_force
INFORMAL STRUCTURE
THEN Identify and test a strategic COG candidate
for ?O2
THEN Identify and test a strategic COG candidate
for a force The force is ?O2
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Use of Disciple at the US Army War College
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 03 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
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Use of Disciple at the US Army War College
589jw Military Applications of Artificial
Intelligence course
Students teach Disciple their COG analysis
expertise, using sample scenarios (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
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Parallel development and merging of knowledge
bases
432 concepts and features, 29 tasks, 18 rules For
COG identification for leaders
Initial KB
Domain analysis and ontology development (KESME)
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
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Other Disciple agents
Disciple-WA (1997-1998) Estimates the best plan
of working around damage to a transportation
infrastructure, such as a damaged bridge or road.
Demonstrated that a knowledge engineer can use
Disciple to rapidly build and update a knowledge
base capturing knowledge from military
engineering manuals and a set of sample solutions
provided by a subject matter expert.
Disciple-COA (1998-1999) Identifies strengths
and weaknesses in a Course of Action, based on
the principles of war and the tenets of army
operations.
Demonstrated the generality of its learning
methods that used an object ontology created by
another group (TFS/Cycorp).
Demonstrated that a knowledge engineer and a
subject matter expert can jointly teach Disciple.
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Disciples vision on the future of software
development
Mainframe Computers
Software systems developed and used by computer
experts
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Vision on the use of Disciple in Education
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Acknowledgements
This research was sponsored by the Defense
Advanced Research Projects Agency, Air Force
Research Laboratory, Air Force Material Command,
USAF under agreement number F30602-00-2-0546, by
the Air Force Office of Scientific Research under
grant number F49620-00-1-0072 and by the US Army
War College.