Transcript Document

11 Learning and problem solving agents
Prof. Gheorghe Tecuci
Learning Agents Laboratory
Computer Science Department
George Mason University
 2003, G.Tecuci, Learning Agents Laboratory
1
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: object ontology + rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
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What are intelligent agents
An intelligent agent is a system that:
• perceives its environment (which may be the physical
world, a user via a graphical user interface, a collection of
other agents, the Internet, or other complex environment);
• reasons to interpret perceptions, draw inferences, solve
problems, and determine actions; and
• acts upon that environment to realize a set of goals or
tasks for which it was designed.
input/
sensors
user/
environment
output/
effectors
Intelligent
Agent
3
The architecture of an intelligent agent
Implements a general problem solving method that uses
the knowledge from the knowledge base to interpret the
input and provide an appropriate output.
Intelligent Agent
Input/
Sensors
User/
Environment
Problem Solving
Engine
Learning
Engine
Output/
Effectors
Knowledge Base
ONTOLOGY
Ontology
OBJECT
Implements
learning
methods
for extending
and refining
the knowledge
in the
knowledge
base.
SUBCLASS-OF
BOOK
CUP
TABLE
INSTANCE-OF
Rules/Cases/…
CUP1
ON
BOOK1
ON
TABLE1
RULE
 x,y,z  OBJECT,
(ON x y) & (ON y z)  (ON x z)
Data structures that represent the objects from the application domain,
general laws governing them, actions that can be performed with them, etc.
4
How are agents built and why it is hard
Intelligent Agent
Domain
Expert
Knowledge
Engineer
Inference Engine
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 expert’s knowledge is long,
painful and inefficient (known as the “knowledge acquisition
5
bottleneck”).
Another approach to agent building
Intelligent Agent
Domain
Expert
Knowledge
Engineer
Inference Engine
Dialog
Programming
Knowledge Base
Results
Instructable Agent
Domain
Expert
Inference Engine
Dialog
Learning Engine
Knowledge Base
Agent training directly by the subject matter expert.
7
Disciple approach to agent building
LALAB has developed a theory, a methodology, and a family of
tools for the rapid building of knowledge bases and agents by
subject matter experts, with limited assistance from knowledge
engineers, to overcome the knowledge acquisition bottleneck.
Disciple approach:
Develop learning and problem solving agents that can be taught by subject matter
experts to become knowledge-based assistants.
The expert teaches the agent to
perform various tasks in a way
that resembles how the expert
would teach a person.
Mixed-initiative problem solving
Teaching and learning
Multistrategy learning
The agent learns from the
expert, building, verifying
and improving its
knowledge base.
Rapid agent
development and
maintenance 8
Disciple’s vision on the future of software development
Mainframe
Computers
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
Software systems
developed and used
by computer experts
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Vision on the use of Disciple in Education
teaches
Disciple
Agent KB
teaches
Disciple
Agent KB
teaches
…
The expert/teacher teaches Disciple
through examples and explanations,
in a way that is similar to how the
expert would teach a student.
Disciple
Agent KB
Disciple
Agent KB
teaches
Disciple tutors the student in a
way that is similar to how the
expert/teacher has taught it. 12
DARPA’s HPKB program: evaluation
Disciple-WA (1997-1998): Estimates the best plan
of working around damage to a transportation
infrastructure, such as a damaged bridge or road.
120
GMU
100
Performance
Disciple-WA 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.
ISI
80
60
Tek/Cyc
40
20
Coverage
0
0%
25000
20000
25%
50%
75%
100%
Coverage
72%increase
increase
of KB
72%
of KB
sizesize
in 17 days
Evolution of KB coverage and performance from
the pre-repair phase to the post-repair phase.
Total Axioms
15000
Rule Axioms
10000
Concept Axioms
5000
Task Axioms
Ontology
Tasks
Rules
7/
1/
99
6/
17
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9
6/
18
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9
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19
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9
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20
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9
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9
0
Knowledge Base
Development of Disciple’s KB during evaluation.
Disciple-WA features:
• High knowledge acquisition rate;
• High problem solving performance
(including unanticipated solutions).
• Demonstrated at EFX’98 as part
14 of an
integrated application led by Alphatech.
DARPA’s HPKB program: evaluation
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.
BLUE-BRIGADE2 attacks to penetrate RED-MECH-REGIMENT2 at 130600 Aug in order to enable the completion of seize
OBJ-SLAM by BLUE-ARMOR-BRIGADE1.
Close:
BLUE-TASK-FORCE1, a balanced task force (MAIN EFFORT) attacks to penetrate RED-MECH-COMPANY4, then clears
RED-TANK-COMPANY2 in order to enable the completion of seize OBJ-SLAM by BLUE-ARMOR-BRIGADE1.
BLUE-TASK-FORCE2, a balanced task force (SUPPORTING EFFORT 1) attacks to fix RED-MECH-COMPANY1 and REDMECH-COMPANY2 and RED-MECH-COMPANY3 in order to prevent RED-MECH-COMPANY1 and RED-MECHCOMPANY2 and RED-MECH-COMPANY3 from interfering with conducts of the MAIN-EFFORT1, then clears REDMECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 and RED-TANK-COMPANY1.
…
Reserve:
The reserve, BLUE-MECH-COMPANY8, a mechanized infantry company, follows Main Effort, and is prepared to reinforce )
MAIN-EFFORT1.
Security:
SUPPORTING-EFFORT1 destroys RED-CSOP1 prior to begin moving across PL-AMBER by MAIN-EFFORT1 in order to
prevent RED-MECH-REGIMENT2 from observing MAIN-EFFORT1.
…
Deep:
Deep operations will destroy RED-TANK-COMPANY1 and RED-TANK-COMPANY2 and RED-TANK-COMPANY3.
Rear:
BLUE-MECH-PLT1, a mechanized infantry platoon secures the brigade support area.
Fires:
Fires will suppress RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 and REDMECH-COMPANY4 and RED-MECH-COMPANY5 and RED-MECH-COMPANY6.
End State: At the conclusion of this operation, BLUE-BRIGADE2 will enable accomplishing conducts forward passage of lines through
BLUE-BRIGADE2 by BLUE-ARMOR-BRIGADE1.
MAIN-EFFORT1 will complete to clear RED-MECH-COMPANY4 and RED-TANK-COMPANY2.
SUPPORTING-EFFORT1 will complete to clear RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECHCOMPANY3 and RED-TANK-COMPANY1.
SUPPORGING-EFFORT2 will complete to clear RED-MECH-COMPANY5 and RED-MECH-COMPANY6 and RED-TANKCOMPANY3.
(Evaluation Items 3, 4, and 5)
160
3 GMU
140
120
Performance
Disciple-COA demonstrated the generality of its
learning methods that used an object ontology
created by another group (TFS/Cycorp).
It also demonstrated that a knowledge
engineer and a subject matter expert can
jointly teach Disciple.
Mission:
4
100%
100
4
4
80
5
5
5
ISI
60
TFS/CyCorp
40
20
3
3
Coverage
0
10000
9000
46% increase of KB
46%
of KB size
sizeincrease
in 8 days
0%
25%
50%
75%
100%
Coverage
Total Axioms
Evolution of KB coverage and performance from the pre-repair
phase to the post-repair phase for the final 3 evaluation items.
8000
7000
6000
5000
Rule Axioms
4000
Concept Axioms
3000
2000
Task Axioms
1000
0
7/8/99
7/9/99
7/10/99
Ontology
7/11/99
Tasks
7/12/99
7/13/99
Rules
7/14/99
7/15/99
7/16/99
Knowledge Base
Development of Disciple’s KB during evaluation.
Disciple-COA features:
• High knowledge acquisition rate;
• Better performance than the other
evaluated systems;
• Better performance than the evaluating
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experts (many unanticipated solutions).
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: object ontology + rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
18
Center of gravity challenge problem
Develop an intelligent agent that is able to identify and test
strategic Center of Gravity candidates for a war scenario.
The center of gravity of an entity (state, alliance, coalition, or
group) is the foundation of capability, the hub of all power and
movement, upon which everything depends, the point against
which all the energies should be directed.
Carl Von Clausewitz, “On War,” 1832.
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.
Giles and Galvin, 1996
19
Approach to center of gravity analysis
Centers of Gravity: Primary sources of moral or physical strength,
power or resistance.
Critical Capabilities: Primary abilities which merit a Center of
Gravity to be identified as such in the context of a given scenario,
situation or mission.
Critical Requirements: Essential conditions, resources and means
for a Critical capability to be fully operative.
Critical Vulnerabilities: Critical Requirements or components
thereof which are deficient, or vulnerable to neutralization,
interdiction or attack (moral/physical harm) in a manner achieving
decisive results – the smaller the resources and effort applied and
the smaller the risk and cost, the better.
Joe Strange,
Centers of Gravity & Critical Vulnerabilities, 1996.
20
First computational approach to COG analysis
• Approach to center of gravity analysis based on the concepts of
critical capabilities, critical requirements and critical vulnerabilities,
which have been recently adopted into the joint military doctrine.
• Application to current war scenarios (e.g. War on terror 2003, Iraq 2003)
with state and non-state actors (e.g. Al Qaeda).
Identification of COG candidates
Identify potential primary
sources of moral or physical
strength, power and
resistance from:
Government
Military
Testing of COG candidates
Test each identified COG
candidate to determine whether
it has all the necessary critical
capabilities:
Which are the critical
capabilities?
People
Are the critical requirements of
these capabilities satisfied?
Economy
If not, eliminate the candidate.
Alliances
If yes, do these capabilities
have any vulnerability?
Etc.
21
Critical capabilities needed to be a COG
people
leader
be protected
stay informed
communicate
military
receive
communication from
the highest level
leadership
be deployable
communicate desires
to the highest level
leadership
be indispensable
be influential
support the goal
be a driving force
have support
be irreplaceable
exert power
industrial capacity
financial capacity
support the highest
level leadership
external support
have a positive impact
will of multi
member force
be influential
ideology
23
Illustration: Saddam Hussein (Iraq 2003)
Critical capability to
be protected
Corresponding critical requirement
Have means to be protected from all threats
Means

Vulnerabilities
Republican Guard Protection Unit
 loyalty not based on conviction and can be influenced by US-led coalition
Iraqi Military
 loyalty can be influenced by US-led coalition
 can be destroyed by US-led coalition
Complex of Iraqi Bunkers  location known to US led coalition
 design known to US led coalition
 can be destroyed by US-led coalition
System of Saddam Doubles
 loyalty of Saddam Doubles to Saddam can be influenced by US-led coalition
24
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.
Teaching
Learning
Disciple
Agent KB
Problem
solving
Global evaluations of Disciple by officers from the Spring 03 course
Strongly
Agree
Agree
Neutral
Disagree
8 Disciple should be used in
7 future versions of this course
6
5
4
3
2
1
0
Strongly
Disagree
Strongly
Agree
Agree
Neutral
Disagree
8Disciple helped me to learn to
7 perform a strategic COG
6 analysis of a scenario
5
4
3
2
1
0
Strongly
Disagree
Strongly
Agree
Agree
Neutral
Disagree
Strongly
Disagree
8 The use of Disciple is an
7
assignment
that is well suited to
6the course's learning objectives
5
4
3
2
1
0
25
Student – Disciple collaboration
Is guided by Disciple to describe the relevant
aspects of a strategic environment.
Develops a formal representation of the scenario.
Identifies and tests strategic COG candidates.
Student
Studies the logic behind COG identification and
testing.
Disciple
Generates a COG analysis report.
Critiques Disciple’s analysis and finalizes the
analysis report.
27
The student is guided by Disciple to describe the
relevant aspects of a strategic scenario.
Disciple represents the scenario as instances in
its object ontology.
Disciple
28
Execution of the elicitation scripts
<object>
Script type: Elicit the feature Has_as_opposing_force
for an instance <scenario-name>
Controls:
Question: Name the opposing forces in <scenario-name>
Answer variable: <opposing-force>
Control type: multiple-line, height 4
Ontology actions:
<opposing-force> instance-of Opposing_force
<scenario-name> Has_as_opposing_force <opposing-force>
Script calls:
Elicit properties of the instance <opposing-force>
Sicily_1943
in new window
subconcept-of
subconcept-of
Force
subconcept-of
Scenario
Opposing_force
instance-of
instance-of
Has_as_opposing_force
Has_as_opposing_force
Allied_Forces_1943
instance-of
European_Axis_1943
…
30
Sample object ontology
<object>
subconcept_of
Scenario
subconcept_of
subconcept_of
subconcept_of
Force
Force_goal
subconcept_of
subconcept_of
subconcept_of Multi_state_force subconcept_of
War_scenario
instance_of Opposing_force
Sicily_1943
subconcept_of Single_state_force
Multi_state_alliance
instance_of
subconcept_of Britain_1943
has as
opposing
force
Equal_partners_
component_
multi_state_
state
alliance
instance_of
instance_of
component_
Allied_Forces_1943
has_as_primary_
force_element
state
subconcept_of
Strategic_COG_relevant_factor
…
subconcept_of
…
subconcept_of
Group
subconcept_of
Single_group_force
subconcept_of
Multi_group_force
Product
subconcept_of
Economic_
factor
…
instance_of
US_1943 has_as_industrial_factor
subconcept_of
instance_of
US_7th_Army_
(Force_343)
instance_of
instance_of
Br_8th_Army_
(Force_545)
has_as_subgroup
instance_of
brief_description
has_as_subgroup
instance_of
has_as_subgroup Western_Naval_TF
Allied_forces_operation_Husky
has_as_subgroup
Eastern_Naval_TF
type_of_operations
has_as_subgroup
instance_of
“WWII Allied invasion
of Sicily in 1943”
Resource_or_
infrastructure_
element
has_as_subgroup
US_9th_Air_Force
instance_of
“combined and joint operations”
Northwest_Africa_Air_Force
subconcept_of
Strategically_
essential_
goods_or_
materiel
…
subconcept_of
Industrial_
factor
…
subconcept_of
Industrial_
capacity
War_materiel_
and_transports
instance_of
War_materiel_
and_transports
_of_US_1943
instance_of
is_a_major_
generator_of
industrial_
capacity_ of_
US_1943
32
Disciple identifies and tests COG candidates
The students study the logic behind COG
identification and testing
33
Disciple generates a COG analysis
report for the student to finalize
35
Demonstration
Disciple as a strategic leader assistant
Disciple
37
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: object ontology + rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
38
Problem solving approach: Task reduction
Disciple uses the task-reduction paradigm
A complex problem solving
task is performed by:
T1
S1
• 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.
T11 S11 … T1n S1n
T111 S111 … T11m S11m
39
Question-answering guided task reduction
Let T1 be the problem solving task to be performed.
Finding a solution is an
iterative process where, at
each step, we consider
some relevant information
that leads us to reduce the
current task to several
simpler tasks.
The question Q associated
with the current task
identifies the type of
information to be
considered.
The answer A identifies that
piece of information and
leads us to the reduction of
the current task.
T1
Q1 S1
A11 S11
…
T11a S11a T11bS11b
…
Q11b S11b
A1n S
1n
T1n
…
A11b1 S11b1… A11bm S11bm
T11b1
T11bm
40
Modeling COG analysis through task reduction
(and solution composition)
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
What kind of scenario is
Sicily_1943?
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
...
Sicily_1943 is a war scenario
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
Identify and test a strategic COG
candidate for Sicily_1943 which is a
war scenario
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
Solution composition
Task reduction
Identify and test a strategic COG
candidate for Sicily_1943
...
41
Identify and test a strategic COG
candidate for Sicily_1943 which is a
war scenario
Which is an opposing force in the
Sicily_1943 scenario?
Allied_Forces_1943
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
...
Identify and test a strategic COG
candidate for Allied_Forces_1943
Is Allied_Forces_1943 a singlemember force or a multi-member
force?
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
Allied_Forces_1943 is a multimember force
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
Identify and test a strategic COG
candidate for Allied_Forces_1943
which is a multi-member force
...
42
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
Identify and test a strategic COG
candidate for Allied_Forces_1943
which is a multi-member force
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
What type of strategic COG
candidate should I consider for this
multi-member force?
...
I consider a candidate
corresponding to the
multi-member nature of
the force
Identify and test a strategic
COG candidate corresponding
to the multi-member nature of
Allied_Forces_1943
I consider a candidate
corresponding to a
member of the multimember force
Identify and test a strategic
COG candidate corresponding
to a member of the
Allied_Forces_1943
The will_of_Allied_Forces_1943 is a
COG candidate with respect to the
cohesion of Allied_Forces_1943
The will of Allied Forces 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
...
43
Identify and test a strategic COG
candidate corresponding to a member
of the Allied_Forces_1943
Which is a member of
Allied_Forces_1943?
The will_of_the_people_of_US_1943 is a
strategic COG candidate with respect to
the people_of_US_1943
The will of people of US 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
...
US_1943
Identify and test a strategic COG
candidate for US_1943
...
What kind of force is US 1943?
US_1943 is a singlemember force
Identify and test a strategic COG
candidate for US_1943 which is a
single-member force
The will_of_the_people_of_US_1943 is a
strategic COG candidate with respect to
the people_of_US_1943
The will of people of US 1943 is a
strategic COG candidate that cannot be
eliminated because it has all the
necessary critical capabilities
...
44
Identify and test a strategic COG
candidate for US 1943 which is a
single-member force
...
What type of strategic COG candidate should I
consider for this single-member force?
I consider a strategic COG candidate
with respect to the people of US
1943
Identify and test a strategic COG
candidate with respect to the people of
US 1943
...
I consider a strategic COG candidate
with respect to the government of
US 1943
Identify and test a strategic COG
candidate with respect to the
government of US 1943
...
I consider a strategic COG candidate
with respect to the armed forces of
US 1943
Identify and test a strategic COG
candidate with respect to the armed
forces of US 1943
...
I consider a candidate corresponding
to the economy US 1943
Identify and test a strategic COG
candidate corresponding to the
economy of US 1943
...
I consider a candidate corresponding
to other sources of moral or physical
strength, power and resistance of US
1943
Identify and test a strategic COG
candidate with respect to other sources
of moral or physical strength, power and
resistance of US 1943
...
45
Identify and test a strategic COG
candidate with respect to the
government of US 1943
President Roosevelt is a strategic COG
candidate with respect to the
government_of_US_1943
Who or what is a main controlling
element of the
government_of_US_1943?
President Roosevelt is a strategic COG
candidate that can be eliminated
because it does not have all the
necessary critical capabilities
President Roosevelt that has a critical
role in setting objectives and making
decisions
Identify President Roosevelt as a
strategic COG candidate with respect
to the government_of_US_1943
President Roosevelt is a strategic COG
candidate with respect to the
government_of_US_1943
Test whether President Roosevelt is a
viable strategic COG candidate
President Roosevelt is a strategic COG
candidate that can be eliminated
because it does not have all the
necessary critical capabilities
46
President Roosevelt is a strategic COG
candidate that can be eliminated
Test whether President Roosevelt is
a viable strategic COG candidate
Which are the critical capabilities that President Roosevelt should have to be a COG candidate?
Does President Roosevelt have all
the necessary critical capabilities?
The necessary critical capabilities are: be protected, stay informed, communicate,
be influential, be a driving force, have support and be irreplaceable
Test whether President
Roosevelt has the critical
capability to be protected
President Roosevelt has the critical capability to be protected. President Roosevelt is
protected by US Service 1943 which has no significant vulnerability
Test whether President
Roosevelt has the critical
capability to stay informed
President Roosevelt has the critical capability to stay informed. President Roosevelt
receives essential intelligence from intelligence agencies which have no significant
vulnerability
Test whether President
Roosevelt has the critical
capability to communicate
President Roosevelt has the critical capability to communicate through executive orders,
through military orders, and through the Mass Media of US 1943. These communication means
have no significant vulnerabilities
Test whether President
Roosevelt has the critical
capability to be influential
President Roosevelt has the critical capability to be influential because he is the head of the
government of US 1943, the commander in chief of the military of US 1943, and is a trusted
leader who can use the Mass Media of US 1943. These influence means have no
significant vulnerabilities.
Test whether President
Roosevelt has the critical
capability to be a driving force
President Roosevelt has the critical capability to be a driving force. The main reason for
President Roosevelt to pursue the goal of unconditional surrender of European Axis is
“preventing separate peace by the members of the Allied Forces”. Also, “the western
democratic values” provides President Roosevelt with determination to persevere in this
goal. There is no significant vulnerability in the reason and determination.
Test whether President
Roosevelt has the critical
capability to have support
President Roosevelt has the critical capability to have support because he is the head of a
democratic government with a history of good decisions, a trusted commander in chief of
the military, and the people are willing to make sacrifices for unconditional surrender of
European Axis. The means to secure continuous support have no significant vulnerability.
Test whether President
Roosevelt has the critical
capability to be irreplaceable
No.
President Roosevelt does not have the critical capability to be irreplaceable. US 1943 would
maintain the goal of unconditional surrender of European Axis irrespective of its leader
because “the goal was established and the country was committed to it”. There is no
significant vulnerability resulted from the replacement of President Roosevelt
47
Test whether President Roosevelt
has the critical capability to be
influential
Which are the critical
requirements for President
Roosevelt to be influential?
President Roosevelt needs means to influence
the government, means to influence the
military and means to influence the people
President Roosevelt has the critical capability to be
influential because he is the head of the government
of US 1943, the commander in chief of the military of
US 1943, and is a trusted leader who can use the
Mass Media of US 1943. These influence means
have no significant vulnerabilities.
Does President Roosevelt
satisfy the critical
requirements to be
influential?
Yes.
Test whether President
Roosevelt has means to
influence the government
President Roosevelt can influence
the government of US 1943
because he is the head of the
government of US 1943. The
influence means have no significant
vulnerability.
Test whether President
Roosevelt has means to
influence the military
President Roosevelt can influence
the military of US 1943 because he
is the commander in chief of the
military of US 1943. The influence
means have no significant
vulnerability.
Test whether President
Roosevelt has means to
influence the people
The influence of President Roosevelt
over the people of US 1943, as a
trusted leader using the Mass Media
of US 1943, has no significant
vulnerability
48
Test whether President
Roosevelt has means to
influence the government
President Roosevelt can influence the
government of US 1943 because he is
the head of the government of US 1943.
The influence means have no significant
vulnerability.
What is a means for President
Roosevelt to influence the
government of US 1943?
President Roosevelt is the head of
the government of US 1943
Test whether the influence of President
Roosevelt over the government of US
1943, as the head of the government of
US 1943, has any significant vulnerability
The influence of President Roosevelt
over the government of US 1943, as
the head of the government of US
1943, has no significant vulnerability
Does the influence of President Roosevelt
over the government of US 1943 have
any significant vulnerability?
No
49
Test whether President
Roosevelt has means to
influence the military
The influence of President Roosevelt over
the military of US 1943, as the commander
in chief of the military of US 1943, has no
significant vulnerability
What is a means for President
Roosevelt to influence the military
of US 1943?
President Roosevelt is the
commander in chief of the military
of US 1943
Test whether the influence of President
Roosevelt over the military of US 1943, as
the commander in chief of the military of
US 1943, has any significant vulnerability
The influence of President Roosevelt over
the military of US 1943, as the commander
in chief of the military of US 1943, has no
significant vulnerability
Does the influence of President Roosevelt
over the military of US 1943 have any
significant vulnerability?
No
50
Test whether President
Roosevelt has means to
influence the people
The influence of President Roosevelt
over the people of US 1943, as a trusted
leader using the Mass Media of US 1943,
has no significant vulnerability
What is a means for President
Roosevelt to influence the people
of US 1943?
President Roosevelt is trusted by the
people of US 1943 and can use Mass
Media of US 1943 to influence them
Test whether the influence of President
Roosevelt over the people of US 1943, as
a trusted leader using the Mass Media of
US 1943, has any significant vulnerability
The influence of President Roosevelt
over the people of US 1943, as a trusted
leader using the Mass Media of US 1943,
has no significant vulnerability
Does the influence of President
Roosevelt over the people of US 1943
have any significant vulnerability?
No
51
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: object ontology + rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
52
The structure of the knowledge base
Knowledge Base = Object ontology + Task reduction rules
The object ontology is a hierarchical description of the
objects from the domain, specifying their properties and
relationships. It includes both descriptions of types of
objects (called concepts) and descriptions of specific
objects (called instances).
The task reduction rules specify generic problem solving
steps of reducing complex tasks to simpler tasks. They are
described using the objects from the ontology.
 2003, G.Tecuci, Learning Agents Laboratory
53
Fragment of the object ontology
governing_body
ad_hoc_ governing_body
established_ governing_body
other_type_of_
governing_body
state_government
group_governing_body
feudal_god_
king_government
other_state_
government
democratic_
government
monarchy
other_
group_
governing_
body
dictator
deity_figure
representative_ parliamentary_
democracy
democracy
government_
of_Italy_1943
totalitarian_
government
police_
state
government_
of_US_1943
government_
of_Britain_1943
military_
dictatorship
religious_
dictatorship
democratic_
council_
or_board
autocratic_
leader
fascist_
state
communist_
dictatorship
 2003, G.Tecuci, Learning Agents Laboratory
government_
of_USSR_1943
theocratic_
government
religious_
dictatorship
chief_and_
tribal_council
theocratic_
democracy
government_
of_Germany_1943
54
Fragment of feature ontology
has_as_controlling_leader
D: agent
R: person
has_as_religious_leader
D: governing_body
R: person
has_as_god_king
D: governing_body
R: person
has_as_monarch
D: governing_body
R: person
has_as_military_leader
D: governing_body
R: person
has_as_political_leader
D: governing_body
R: person
has_as_head_of_government
D: governing_body
R: person
 2003, G.Tecuci, Learning Agents Laboratory
has_as_commander_in_chief
D: force
R: person
has_as_head_of_state
D: governing_body
R: person
56
Sample task
A task is a representation of anything
that an agent may be asked to perform.
General task:
Identify and test a strategic COG candidate
corresponding to the ?O1
INFORMAL STRUCTURE OF THE TASK
Identify and test a strategic COG candidate
corresponding to the economy of a force
The economy is ?O1
Condition
?O1 is type_of_economy
FORMAL STRUCTURE OF THE TASK
Instantiated task:
Identify and test a strategic COG candidate
corresponding to the economy_of_US_1943
INFORMAL STRUCTURE OF THE TASK
 2003, G.Tecuci, Learning Agents Laboratory
Identify and test a strategic COG candidate
corresponding to the economy of a force
The economy is economy_of_US_1943
FORMAL STRUCTURE OF THE TASK
58
Exercise
How could the agent generate plausible formalizations?
Identify and test a strategic COG
candidate for Sicily_1943
What kind of scenario is
Sicily_1943?
Sicily_1943 is a war scenario
Identify and test a strategic COG
candidate for Sicily_1943 which is a
war scenario
 2003, G.Tecuci, Learning Agents Laboratory
59
Sample task reduction rule
IF
Identify and test a strategic COG candidate
corresponding to the ?O1 which is an
industrial_economy
IF
Identify and test a strategic COG candidate
corresponding to the economy of a force
which is an industrial economy
The industrial economy is ?O1
Question
Who or what is a strategically critical
element with respect to the ?O1 ?
Answer
?O2 because it is an essential generator
of war_materiel for ?O3 from the strategic
perspective
Condition
?O1 is industrial_economy
THEN
Identify ?O2 as a COG candidate with
respect to the ?O1
?O4 is force
has_as_economy ?O1
has_as_industrial_factor ?O2
Test ?O2 which is a strategic COG candidate
with respect to the ?O1
THEN
Identify a strategically critical element as a
COG candidate with respect to an industrial
economy
The strategically critical element is ?O2
The industrial economy is ?O1
INFORMAL STRUCTURE OF THE RULE
FORMAL STRUCTURE OF THE RULE
A rule is an ontology-based representation
of an elementary problem solving step.
 2003, G.Tecuci, Learning Agents Laboratory
?O2 is industrial_capacity
generates_essential_war_materiel_from_
the_strategic_perspective_of ?O3
?O3 is multi_state_force
has_as_member ?O4
Test a strategically critical element which is a
strategic COG candidate with respect to an
industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
61
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: Object ontology + Rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
63
Illustration of rule-based task reduction
Identify and test a strategic COG candidate corresponding
to the economy of a force which is an industrial economy
The industrial economy is economy_of_US_1943
?O1  economy_of_US_1943
Rule condition
industrial_economy
instance-of
force
economy_of_US_1943
instance-of
has_as_economy
?O4
has_as_industrial_factor
instance-of
has_as_member
instance-of
?O3
Condition
?O1 is industrial_economy
?O2
industrial_capacity
multi_state_force
IF
Identify and test a strategic COG candidate corresponding
to the economy of a force which is an industrial economy
The industrial economy is ?O1
?O2
generates_essential_
war_materiel_from_ the_
strategic_perspective_of
is industrial_capacity
generates_essential_war_materiel_from_
the_strategic_perspective_of ?O3
?O3 is multi_state_force
has_as_member ?O4
?O4 is force
has_as_economy ?O1
has_as_industrial_factor ?O2
THEN
Identify a strategically critical element as a COG candidate
with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
Test a strategically critical element which is a strategic
COG candidate with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
 2003, G.Tecuci, Learning Agents Laboratory
64
Matchings
Object ontology
Rule condition
force
industrial_economy
instance-of
single_member_force
economy_of_US_1943
subconcept-of
force
instance-of
?O4
instance-of
economy_of_US_1943
industrial_capacity
has_as_industrial_factor
instance-of
multi_state_force
instance-of
industrial_economy
single_state_force
has_as_economy
has_as_member
subconcept-of
?O2
generates_essential_
war_materiel_from_ the_
strategic_perspective_of
?O3
?O2  industrial_capacity_of_US_1943
instance-of
has_as_economy
US_1943
industrial_capacity
has_as_industrial_factor
instance-of
Industrial_capacity_
multi_state_force
of_US_1943
subconcept-of
multi_state_
alliance
subconcept-of
generates_essential_
equal_partner_
war_materiel_from_ the_
multi_state_
strategic_perspective_of
alliance
?O3  Allied_forces_1943
?O4  US_1943
has_as_member
instance-of
Allied_forces_1943
 2003, G.Tecuci, Learning Agents Laboratory
66
Identify and test a strategic COG candidate corresponding
to the economy of a force which is an industrial economy
The industrial economy is economy_of_US_1943
Rule condition
industrial_economy
instance-of
force
economy_of_US_1943
instance-of
has_as_economy
?O4
industrial_capacity
has_as_industrial_factor
instance-of
multi_state_force
has_as_member
instance-of
?O2
generates_essential_
war_materiel_from_ the_
strategic_perspective_of
?O1  economy_of_US_1943
IF
Identify and test a strategic COG candidate corresponding
to the economy of a force which is an industrial economy
The industrial economy is ?O1
Condition
?O1 is industrial_economy
?O2
is industrial_capacity
generates_essential_war_materiel_from_
the_strategic_perspective_of ?O3
?O3 is multi_state_force
has_as_member ?O4
?O4 is force
has_as_economy ?O1
has_as_industrial_factor ?O2
THEN
Identify a strategically critical element as a COG candidate
with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
?O3
Test a strategically critical element which is a strategic
COG candidate with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
Identify a strategically critical element as a COG candidate with respect
to an industrial economy
?O1  economy_of_US_1943
The strategically critical element is industrial_capacity_of_US_1943
The industrial economy is economy_of_US_1943
?O2  industrial_capacity_of_US_1943
Test a strategically critical element which is a strategic COG candidate
with respect to an industrial economy
The strategically critical element is industrial_capacity_of_US_1943
The industrial economy is economy_of_US_1943
 2003, G.Tecuci, Learning Agents Laboratory
?O3  Allied_forces_1943
?O4  US_1943
68
Generating the informal reduction
Identify and test a strategic COG candidate
corresponding to the economy_of_US_1943
which is an industrial_economy
?O1  economy_of_US_1943
IF
Identify and test a strategic COG candidate
corresponding to the ?O1 which is an
industrial_economy
Who or what is a strategically critical
element with respect to the
economy_of_US_1943?
industrial_capacity_of_US_1943
because it is an essential generator
of war materiel for Allied_forces_1943
from the strategic perspective
Identify industrial_capacity_of_US_1943 as a
COG candidate with respect to the
economy_of_US_1943
Test industrial_capacity_of_US_1943 which
is a strategic COG candidate with respect to
the economy_of_US_1943
 2003, G.Tecuci, Learning Agents Laboratory
Question
Who or what is a strategically critical
element with respect to the ?O1 ?
Answer
?O2 because it is an essential generator of
war_materiel for ?O3 from the strategic
perspective
THEN
Identify ?O2 as a COG candidate with
respect to the ?O1
Test ?O2 which is a strategic COG candidate
with respect to the ?O1
?O1  economy_of_US_1943
?O2  industrial_capacity_of_US_1943
?O3  Allied_forces_1943
?O4  US_1943
70
Successive rule applications
Identify and test a strategic COG candidate
corresponding to the economy_of_US_1943
Rule_1
What is the type of economy_of_US_1943 ?
industrial_economy
Rule_2
Identify and test a strategic COG candidate
corresponding to the economy_of_US_1943
which is an industrial_economy
Who or what is a strategically critical
element with respect to the
economy_of_US_1943?
industrial_capacity_of_US_1943
because it is an essential generator
of war materiel for Allied_forces_1943
from the strategic perspective
Identify industrial_capacity_of_US_1943 as a
COG candidate with respect to the
economy_of_US_1943
 2003, G.Tecuci, Learning Agents Laboratory
Test industrial_capacity_of_US_1943 which
is a strategic COG candidate with respect to
the economy_of_US_1943
71
Task reduction rule with “Except when” conditions
IF
<task>
Condition
<condition 1>
Except when condition
<condition 2>
In addition to the regular
rule condition that needs to
be satisfied, a rule may
contain one or several
except when conditions that
should not be satisfied for
the rule to be applicable.
Except when condition
<condition n>
THEN
<subtask 1>
…
<subtask m>
 2003, G.Tecuci, Learning Agents Laboratory
72
Plausible
version
space
rule
IF
Identify and test a strategic COG candidate corresponding to the economy of a
force which is an industrial economy
The industrial economy is ?O1
Plausible upper bound condition
?O1 is type_of_economy
?O2 is economic_factor
generates_essential_war_materiel_from_the_strategic_perspective_of ?O3
?O3 is multi_state_force
has_as_member ?O4
?O4 is force
has_as_economy ?O1
has_as_industrial_factor ?O2
Plausible lower bound condition
?O1 is industrial_economy
?O2 is industrial_capacity
generates_essential_war_materiel_from_the_strategic_perspective_of ?O3
?O3 is multi_state_alliance
has_as_member ?O4
?O4 is single_state_force
has_as_economy ?O1
has_as_industrial_factor ?O2
THEN
Identify a strategically critical element as a COG candidate with respect to an
industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
Test a strategically critical element which is a strategic COG candidate with
respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
73
 2003, G.Tecuci, Learning Agents Laboratory
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: Object ontology + Rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
75
The search space for problem solving
Let us consider the problem solving task 'Pa‘ and let R1, R2, and R3 be
the applicable rules which indicate the reduction of 'Pa' to ‘C(Pb,Pc)', to
'Pd', and to ‘C(Pe,Pf,Pg)', respectively. Therefore, to solve the problem
'Pa', one may either:
- solve the problems 'Pb' and 'Pc', or
- solve the problem 'Pd', or
- solve the problems 'Pe', 'Pf' and 'Pg'.
One may represent all these alternatives in the form of an AND/OR tree.
Pa
R1
R2
C(Pb,P c)
Pb
 2003, G.Tecuci, Learning Agents Laboratory
R3
Pd
Pc
C(Pe,P f,P g)
Pe
Pf
Pg
76
The search space for problem solving (cont.)
The node 'Pa' is called an OR node since for solving the problem 'Pa' it is
enough to solve ‘C(Pb, Pc)' OR to solve 'Pd' OR to solve ‘C(Pe, Pf, Pg)'.
The node ‘C(Pb, Pc)' is called an AND node since for solving it one must
solve both 'Pb' AND 'Pc'.
Pa
R1
R2
C(Pb,P c)
Pb
R3
Pd
Pc
C(Pe,P f,P g)
Pe
Pf
Pg
The AND/OR tree may be further developed by considering all the rules
applicable to its leaves (Pb, Pc, Pd, Pe, Pf, Pg), building the entire search
space for the problem 'Pa'. This space contains all the solutions to 'Pa'.
 2003, G.Tecuci, Learning Agents Laboratory
77
Solution tree
To find a solution one needs only to build enough of the tree to
demonstrate that 'Pa' is solved. Such a tree is called a solution tree.
A node is solved in one of the following cases:
- it is a terminal node (a primitive task with known solution);
- it is an AND node whose successors are solved;
- it is an OR node which has at least one solved successor.
Pa
R1
R2
C(Pb,P c)
Pb
 2003, G.Tecuci, Learning Agents Laboratory
R3
Pd
Pc
C(Pe,P f,P g)
Pe
Pf
Pg
78
Solution tree (cont.)
Once the problem solver detects that a node is solved it sends this
information to the ancestors of the node. When the node 'Pa' becomes
solved, one has found a solution to 'Pa'.
P a solved
R1
R2
C(Pb,P c)
Pb
 2003, G.Tecuci, Learning Agents Laboratory
R3
C(Pe,P f,P g) solved
Pd
Pc
Pe
Pf
Pg
solved
solved
solved
79
Solution tree (cont.)
A node is unsolvable in one of the following cases:
- it is a nonterminal node that has no successors
(i.e. a nonprimitive problem to which no rule applies);
- it is an AND node which has at least one unsolvable successor;
- it is an OR node which has all the successors unsolvable.
Pa
R1
R2
C(Pb,P c)
Pb
 2003, G.Tecuci, Learning Agents Laboratory
R3
Pd
Pc
C(Pe,P f,P g)
Pe
Pf
Pg
80
Solution tree (cont.)
Once the problem solver detects that a node is unsolvable it sends this
information to the ancestors of the node. If the node 'Pa' becomes
unsolvable, then no solution to 'Pa' exists.
Pa
unsolvable C(Pb,P c)
C(Pe,P f,P g)
Pd
Pb
Pc
solved
unsolvable
 2003, G.Tecuci, Learning Agents Laboratory
R3
R2
R1
Pe
Pf
Pg
81
General search strategies
The presented method assumes an exhaustive search of the solution space.
Usually, however, the real world problems are characterized by huge search
spaces and one has to use heuristic methods in order to limit the search.
What types of search control decisions can you identify?
Attention focusing: What problem, among the leaves of the problem solving
tree, to reduce next?
Meta-rule: What rule, among the applicable ones, to use for reducing the current
problem?
 2003, G.Tecuci, Learning Agents Laboratory
82
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: Object ontology + Rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
83
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
Strongly
Agree
Agree
Neutral
Spring 2003
COG testing based on
critical capabilities
Disagree
9
8
7
6
5
4
3
2
1
0
Strongly
Disagree
Strongly
Agree
Agree
Neutral
Spring 2002
COG identification
and testing
Disagree
9
8
7
6
5
4
3
2
1
0
Strongly
Disagree
Strongly
Agree
 2003, G.Tecuci, Learning Agents Laboratory
Agree
Neutral
Disagree
Spring 2001
COG identification
Strongly
Disagree
9
8
7
6
5
4
3
2
1
0
84
Control of modeling, learning and problem solving
Input Task
Mixed-Initiative
Problem Solving
Ontology + Rules
Generated Reduction
New Reduction
Accept Reduction
Reject Reduction
Solution
Modeling
Formalization
Task Refinement
Rule Refinement
Rule Refinement
Learning
 2003, G.Tecuci, Learning Agents Laboratory
86
I need to
Identify and test a strategic COG candidate corresponding
to a member of the Allied_Forces_1943
1
Which is a member of Allied_Forces_1943?
Provides
an example
2
Learns
Rule_15
US_1943
Therefore I need to
Identify and test a strategic COG candidate for US_1943
…
I need to
3
Identify and test a strategic COG candidate corresponding
to a member of the European_Axis_1943
Applies
Rule_15
Which is a member of European_Axis_1943?
?
4
Accepts the
example
 2003, G.Tecuci, Learning Agents Laboratory
5
Refines
Rule_15
Germany_1943
Therefore I need to
Identify and test a strategic COG candidate for Germany_1943
88
Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: Object ontology + Rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
 2003, G.Tecuci, Learning Agents Laboratory
90
The rule learning problem: definition
GIVEN:
• an example of a problem solving episode;
• a knowledge base that includes an object ontology and
a set of problem solving rules;
• an expert that understands why the given example is
correct and may answer agent’s questions.
DETERMINE:
• a plausible version space rule that is an analogy-based
generalization of the specific problem solving episode.
 2003, G.Tecuci, Learning Agents Laboratory
91
Input example
Which is a member of
Allied_Forces_1943?
US_1943
I need to
Identify and test a strategic COG
candidate corresponding to a member of
the Allied_Forces_1943
Therefore I need to
Identify and test a strategic COG
candidate for US_1943
This is an example of a problem solving step from which
the agent will learn a general problem solving rule.
 2003, G.Tecuci, Learning Agents Laboratory
92
Learned PVS rule
IF
Identify and test a strategic COG
candidate corresponding to a
member of the ?O1
IF
Identify and test a strategic COG candidate
corresponding to a member of a force
The force is ?O1
Question
Which is a member of ?O1 ?
Answer
?O2
explanation
?O1 has_as_member ?O2
THEN
Identify and test a strategic COG
candidate for ?O2
INFORMAL STRUCTURE OF THE RULE
FORMAL STRUCTURE OF THE RULE
 2003, G.Tecuci, Learning Agents Laboratory
Plausible Upper Bound Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is force
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance
has_as_member ?O2
?O2 is single_state_force
THEN
Identify and test a strategic COG candidate
for a force
The force is ?O2
93
Basic steps of the rule learning method
1. Formalize and learn the tasks
2. Find a formal explanation of why the example is correct.
This explanation is the best possible approximation of the
question and the answer, in the object ontology.
3. Generalize the example and the explanation into a plausible
version space rule.
 2003, G.Tecuci, Learning Agents Laboratory
95
1. Formalize the tasks
I need to
Identify and test a strategic COG
candidate corresponding to a
member of the Allied_Forces_1943
I need to
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is Allied_Forces_1943
Therefore I need to
Identify and test a strategic COG
candidate for US_1943
Therefore I need to
Identify and test a strategic COG
candidate for a force
The force is US_1943
 2003, G.Tecuci, Learning Agents Laboratory
96
Task learning
Identify and test a strategic COG candidate for US_1943
Identify and test a strategic COG candidate for a force
The force is US_1943
Identify and test a strategic COG candidate
for ?O1
object
subconcept_of
INFORMAL STRUCTURE OF THE TASK
force
subconcept_of
subconcept_of
opposing_force
Identify and test a strategic COG candidate
for a force
The force is ?O1
multi_member_force
subconcept_of
subconcept_of
multi_state_force
instance_of
single_member_force
subconcept_of
instance_of
multi_state_alliance
Plausible upper bound condition
?O1 is force
Plausible lower bound condition
?O1 is single_state_force
subconcept_of
equal_partners_
multi_state_
alliance
instance_of
FORMAL STRUCTURE OF THE TASK
 2003, G.Tecuci, Learning Agents Laboratory
Allied_Forces_1943
Single_state_force
has_as_member
instance_of
US_1943
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2. Find an explanation of why the example is correct
Which is a member of
Allied_Forces_1943?
US_1943
I need to
Identify and test a strategic COG
candidate corresponding to a member of
the Allied_Forces_1943
Therefore I need to
Identify and test a strategic COG
candidate for US_1943
The explanation is the best possible approximation of
the question and the answer, in the object ontology.
Allied_Forces_1943
 2003, G.Tecuci, Learning Agents Laboratory
has_as_member
US_1943
100
3. Generate the PVS rule
Allied_Forces_1943
has_as_member
US_1943
IF
Identify and test a strategic COG candidate
corresponding to a member of a force
The force is ?O1
Rewrite
as
Most general
generalization
Condition
?O1 is Allied_Forces_1943
has_as_member ?O2
?O2 is US_1943
Most specific
generalization
explanation
?O1 has_as_member ?O2
Plausible Upper Bound Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is force
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance
has_as_member ?O2
?O2 is single_state_force
THEN
Identify and test a strategic COG candidate
for a force
The force is ?O2
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Analogical reasoning
Analogy
criterion multi_member_force
force
instance_of
?O1
instance_of
has_as_
member
less general than
explanation
has_as_
Allied_Forces_1943 member US_1943
less general than
Identify and test a strategic COG
candidate for a force
The force is US_1943
 2003, G.Tecuci, Learning Agents Laboratory
similar explanation
similar
has_as_
European_Axis_1943 member Germany_1943
explains?
explains
initial example
I need to
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is Allied_Forces_1943
Therefore I need to
?O2
similar
similar example
I need to
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is European_Axis_1943
Therefore I need to
Identify and test a strategic COG
candidate for a force
The force is Germany_1943
104
Generalization by analogy
multi_member_force
has_as_
Allied_Forces_1943 member US_1943
instance_of
?O1
explains
initial example
generalization
force
instance_of
has_as_
member
?O2
explains
I need to
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is Allied_Forces_1943
Therefore I need to
I need to
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is ?O1
Therefore I need to
Identify and test a strategic COG
candidate for a force
The force is US_1943
Identify and test a strategic COG
candidate for a force
The force is ?O2
Knowledge-base constraints on the generalization:
Any value of ?O1 should be an instance of:
DOMAIN(has_as_member)  RANGE(The force is) =
multi_member_force  force = multi_member_force
Any value of ?O2 should be an instance of:
RANGE(has_as_member)  RANGE(The force is) = force  force = force
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Overview
Learning and problem solving agents: Disciple
An agent for center of gravity analysis
Modeling of problem solving through task reduction
Knowledge base: Object ontology + Rules
Rule-based problem solving
Control of the problem solving process
Control of modeling, learning and problem solving
Multistrategy rule learning
Multistrategy rule refinement
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4. The rule refinement problem (definition)
GIVEN:
• a plausible version space rule;
• a positive or a negative example of the rule (i.e. a correct or
an incorrect problem solving episode);
• a knowledge base that includes an object ontology and a set
of problem solving rules;
• an expert that understands why the example is positive or
negative, and can answer agent’s questions.
DETERMINE:
• an improved rule that covers the example if it is positive, or
does not cover the example if it is negative;
• an extended object ontology (if needed for rule refinement).
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Version space rule learning and refinement
Let E1 be the first task reduction from which the rule is learned.
The agent learns a rule with a very specific
lower bound condition (LB) and a very
general upper bound condition (UB).
UB
LB+E1
Let E2 be a new task reduction generated
by the agent and accepted as correct by
the expert. Then the agent generalizes LB
as little as possible to cover it.
UB
Let E3 be a new task reduction generated
by the agent which is rejected by the
expert. Then the agent specialize UB as
little as possible to uncover it and to
remain more general than LB.
UB
After several iterations of this process LB
may become identical with UB and a rule
with an exact condition is learned.
UB=LB
_ + + +
+
 2003, G.Tecuci, Learning Agents Laboratory
LB+
+E2
LB+
+
_ E3
…
_
_
110
Rule refinement with a positive example
Positive example that
satisfies the upper bound
I need to
Identify and test a strategic COG
candidate corresponding to a
member of the European_Axis_1943
explanation
?O1 has_as_member ?O2
Therefore I need to
Identify and test a strategic COG
candidate for Germany_1943
Plausible Upper Bound Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is force
less general than
IF
Identify and test a strategic COG candidate
corresponding to a member of a force
The force is ?O1
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance
has_as_member ?O2
?O2 is single_state_force
THEN
Identify and test a strategic COG candidate
for a force
The force is ?O2
 2003, G.Tecuci, Learning Agents Laboratory
Condition satisfied
by positive example
?O1 is European_Axis_1943
has_as_member ?O3
?O2 is Germany_1943
explanation
European_Axis_1943
has_as_member Germany_1943
111
Minimal generalization of the plausible lower bound
Plausible Upper Bound Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is
force
less general than (or at most as general as)
New Plausible Lower Bound Condition
?O1 is multi_state_alliance
has_as_member
?O2
is
?O2
single_state_force
minimal generalization
Plausible Lower Bound Condition (from rule) Condition satisfied by the positive example
?O1 is equal_partners_multi_state_alliance
has_as_member ?O2
?O1 is European_Axis_1943
has_as_member ?O2
?O2 is single_state_force
?O2 is Germany_1943
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Forces
composition_of_forces
force
single_member_force
single_state_force
US_1943
multi_member_force
single_group_force
Germany_1943
multi_state_force
multi_state_alliance
multi_state_alliance
is the minimal generalization of
equals_partners_multi_state_alliance
that covers European_Axis_1943
dominant_partner_
multi_state_alliance
European_Axis_1943
…
multi_state_coalition
dominant_partner_
multi_state_coalition
equal_partners_
multi_state_alliance
Allied_Forces_1943
 2003, G.Tecuci, Learning Agents Laboratory
multi_group_force
equal_partners_
multi_state_coalition
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Refined rule
IF
Identify and test a strategic COG
candidate corresponding to a
member of a force
The force is ?O1
explanation
?O1 has_as_member ?O2
explanation
?O1 has_as_member ?O2
Plausible Upper Bound Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is force
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance
has_as_member ?O2
?O2 is single_state_force
THEN
Identify and test a strategic COG candidate
for a force
The force is ?O2
 2003, G.Tecuci, Learning Agents Laboratory
generalization
IF
Identify and test a strategic COG candidate
corresponding to a member of a force
The force is ?O1
Plausible Upper Bound
Condition
?O1 is multi_member_force
has_as_member ?O2
?O2 is force
Plausible Lower Bound Condition
?O1 is multi_state_alliance
has_as_member ?O2
?O2 is single_state_force
THEN
Identify and test a strategic COG
candidate for a force
The force is ?O2
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Demonstration
Teaching Disciple to test leaders who are COG
candidates
Disciple
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Recommended reading
Tecuci G., Building Intelligent Agents: A Theory, Methodology, Tool and
Case Studies, Academic Press, 1998.
Tecuci G., Boicu M., Bowman M., and Marcu M., with a commentary by
Burke M.: An Innovative Application from the DARPA Knowledge Bases
Programs: Rapid Development of a High Performance Knowledge Base
for Course of Action Critiquing, in AI Magazine, 22, 2, 2001, pp. 43-61.
AAAI Press, Menlo Park, California, 2001.
http://lalab.gmu.edu/publications/default.htm Describes the course of
action domain.
Tecuci G., Boicu M., Marcu D., Stanescu B., Boicu C. and Comello J.,
Training and Using Disciple Agents: A Case Study in the Military Center
of Gravity Analysis Domain, in AI Magazine, AAAI Press, Menlo Park,
California, 2002. http://lalab.gmu.edu/publications/default.htm
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