Download - Multistrategy Rule Refinement
G.Tecuci, Learning Agents Laboratory
Learning Agents LaboratoryDepartment of Computer Science
George Mason University
Gheorghe Tecuci [email protected]://lalab.gmu.edu/
CS 785, Fall 2001
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement in other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Hands-on experience: Problem solving and learning
G.Tecuci, Learning Agents Laboratory
The rule refinement methodThe rule refinement method
General presentation of the rule refinement method
Rule refinement with a positive example
Rule refinement with a negative example
The rule refinement problem
Characterization of the learned PVS rule
G.Tecuci, Learning Agents Laboratory
The rule refinement problemThe rule refinement problem
GIVEN:
• a plausible version space rule R;
• a positive or a negative example E of the rule (i.e. a correct or an incorrect problem solving episode that has the same IF and THEN tasks as R);
• 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).
G.Tecuci, Learning Agents Laboratory
The rule refinement methodThe rule refinement method
General presentation of the rule refinement method
Rule refinement with a positive example
Rule refinement with a negative example
The rule refinement problem
Characterization of the learned PVS rule
G.Tecuci, Learning Agents Laboratory
The rule refinement method: general presentationThe rule refinement method: general presentation
Let R be a plausible version space rule, U its plausible upper bound condition, L its plausible lower bound condition, and E a new example of the rule.
1. If E is covered by U but it is not covered by L then
• If E is a positive example then L needs to be generalized as little as possible to cover it while remaining less general or at most as general as U.
• If E is a negative example then U needs to be specialized as little as possible to no longer cover it while remaining more general than or at least as general as L. Alternatively, both bounds need to be specialized.
2. If E is covered by L then
• If E is a positive example then R need not to be refined.
• If E is a negative example then both U and L need to be specialized as little as possible to no longer cover this example while still covering the known positive examples of the rule. If this is not possible, then the E represents a negative exception to the rule.
3. If E is not covered by U then
• If E is a positive example then it represents a positive exception to the rule.
• If E is a negative example then no refinement is necessary.
G.Tecuci, Learning Agents Laboratory
1. If E is covered by U but it is not covered by L then
• If E is a positive example then L needs to be generalized as little as possible to cover it while remaining less general or at most as general as U.
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
+++
UBLB
+
G.Tecuci, Learning Agents Laboratory
1. If E is covered by U but it is not covered by L then
• If E is a negative example then U needs to be specialized as little as possible to no longer cover it while remaining more general than or at least as general as L.
Alternatively, both bounds need to be specialized.
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
UB_++
LB_
Strategy 1:Specialize UB by using a specialization rule (e.g. the descending the generalization hierarchy rule, or specializing a numeric interval rule).
G.Tecuci, Learning Agents Laboratory
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
++
UBLB
_
EXw identifies the features that make E a wrong problem solving episode.The inductive hypothesis is that the correct problem solving episodes should not have these features.EXw is taken as an example of a condition that the correct problem solving episodes should not satisfy, an Except-When condition.The Except-when condition needs also to be learned, based on additional examples.Based on EXw an initial Except-When plausible version space condition is generated.
Strategy 2:Find a failure explanation EXw of why E is a wrong problem solving episode.
G.Tecuci, Learning Agents Laboratory
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
++
UBLB
_
Specialize both bounds of the plausible version space condition by: - adding the most general generalization of EXw, corresponding to the examples encountered so far, to the upper bound; - adding the least general generalization of EXw, corresponding to the examples encountered so far, to the lower bound.
Strategy 3:Find an additional explanation EXw for the correct problem solving episodes, which is not satisfied by the current wrong problem solving episode.
_
G.Tecuci, Learning Agents Laboratory
2. If E is covered by L then
• If E is a positive example then R need not to be refined.
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
+
G.Tecuci, Learning Agents Laboratory
2. If E is covered by L then
• If E is a negative example then both U and L need to be specialized as little as possible to no longer cover this example while still covering the known positive examples of the rule. If this is not possible, then the E represents a negative exception to the rule.
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
- ++
UBLB
-
Strategy 1:Find a failure explanation EXw of why E is a wrong problem solving episode and create an Except-When a plausible version space condition, as indicated before.
G.Tecuci, Learning Agents Laboratory
3. If E is not covered by U then
• If E is a positive example then it represents a positive exception to the rule. • If E is a negative example then no refinement is necessary.
The rule refinement method: general presentationThe rule refinement method: general presentation
++
UBLB
-
++
UBLB
+
G.Tecuci, Learning Agents Laboratory
The rule refinement methodThe rule refinement method
General presentation of the rule refinement method
Rule refinement with a positive example
Rule refinement with a negative example
The rule refinement problem
Characterization of the learned PVS rule
G.Tecuci, Learning Agents Laboratory
Positive example covered by the upper boundPositive example covered by the upper bound
Condition satisfied by positive example
?O1 IS Germany_1943has_as_industrial_factor ?O2
?O2 IS Industrial_capacity_of_Germany_1943 is_a_major_generator_of ?O3
?O3 IS War_materiel_and_fuel_of_Germany_1943less
gen
eral
th
an
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
Plausible Lower Bound Condition
?O1 IS US_1943has_as_industrial_factor ?O2
?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3
?O3 IS War_materiel_and_transports_of_US_1943
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O3
Identify the strategic COG candidates with respect to the industrial civilization of a force
The force is Germany_1943
A strategic COG relevant factor is strategic COG candidate for a force
The force is Germany_1943The strategic COG relevant factor is
Industrial_capacity_of_Germany_1943
IF the task to accomplish is
THEN accomplish the task
Positive example that satisfies the upper bound
explanationGermany_1943 has_as_industrial_factor
Industrial_capacity_of_Germany_1943Industrial_capacity_of_Germany_1943 is_a_major_generator_of War_materiel_and_fuel_of_Germany_1943
G.Tecuci, Learning Agents Laboratory
Condition satisfied by the positive example
?O1 IS Germany_1943has_as_industrial_factor ?O2
?O2 IS Industrial_capacity_of_Germany_1943 is_a_major_generator_of ?O3
?O3 IS War_materiel_and_fuel_of_Germany_1943
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
Plausible Lower Bound Condition (from rule)
?O1 IS US_1943has_as_industrial_factor ?O2
?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3
?O3 IS War_materiel_and_transports_of_US_1943
Minimal generalization of the plausible lower boundMinimal generalization of the plausible lower bound
New Plausible Lower Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
minimal generalization
less general than (or at most as general as)
G.Tecuci, Learning Agents Laboratory
Opposing_force
Force
Single_state_force Single_group_forceMulti_group_forceMulti_state_force
Generalization hierarchy of forces Generalization hierarchy of forces
Anglo_allies_1943
European_axis_1943
US_1943
Britain_1943
Germany_1943
component_state
Italy_1943
component_state
component_state
component_state
Group
<object>
G.Tecuci, Learning Agents Laboratory
Generalized ruleGeneralized rule
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O4
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O3
Plausible Lower Bound Condition
?O1 IS US_1943has_as_industrial_factor ?O2
?O2 IS Industrial_capacity_of_US_1943 is_a_major_generator_of ?O3
?O3 IS War_materiel_and_transports_of_US_1943
Plausible Upper Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
G.Tecuci, Learning Agents Laboratory
The rule refinement methodThe rule refinement method
General presentation of the rule refinement method
Rule refinement with a positive example
Rule refinement with a negative example
The rule refinement problem
Characterization of the learned PVS rule
G.Tecuci, Learning Agents Laboratory
A negative example covered by the upper boundA negative example covered by the upper bound
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O3
Plausible Upper Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
Condition satisfied by positive example
?O1 IS Italy_1943has_as_industrial_factor ?O2
?O2 IS Farm_implement_industry_of_Italy_1943 is_a_major_generator_of ?O3
?O3 IS Farm_implements_of_Italy_1943le
ss g
ener
al t
han
Identify the strategic COG candidates with respect to the industrial civilization of a force
The force is Italy_1943
A strategic COG relevant factor is strategic COG candidate for a force
The force is Italy_1943The strategic COG relevant factor is
Farm_implement_industry_of_Italy_1943
IF the task to accomplish is
THEN accomplish the task
Negative example that satisfies the upper bound
explanationItaly_1943 has_as_industrial_factor
Farm_implement_industry_of_Italy_1943Farm_implement_industry_of_Italy_1943 is_a_major_generator_of
Farm_implements_of_Italy_1943
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
G.Tecuci, Learning Agents Laboratory
IF
THEN
Automatic generation of plausible explanationsAutomatic generation of plausible explanations
Industrial_capacity_of_Italy_1943is a strategic COG candidate for Italy_1943
Identify the strategic COG candidates with respect to the industrial civilization of Italy_1943
The agent generates a list of plausible explanations from which the expert has to select the correct one:
Farm_implements_of_Italy_1943 IS_NOTStrategically_essential_goods_or_materiel
Farm_implement_industry_of_Italy_1943 IS_NOT Industrial_capacity
explanationItaly_1943 has_as_industrial_factor
Farm_implement_industry_of_Italy_1943Farm_implement_industry_of_Italy_1943 is_a_major_generator_of
Farm_implements_of_Italy_1943
Who or what is a strategicallycritical industrial civilization
element in Italy_1943?
Industrial_capacity_of_Italy_1943
No!
G.Tecuci, Learning Agents Laboratory
Minimal specialization of the plausible upper boundMinimal specialization of the plausible upper bound
Plausible Upper Bound Condition (from rule)?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
Condition satisfied by the negative example
?O1 IS Italy_1943has_as_industrial_factor ?O2
?O2 IS Farm_implement_industry_of_Italy_1943 is_a_major_generator_of ?O3
?O3 IS Farm_Implements_of_Italy_1943
New Plausible Upper Bound Condition
?O1 IS Forcehas_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materiel
New Plausible Lower Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materiel
more general than(or at least as general as)
specialization
G.Tecuci, Learning Agents Laboratory
Fragment of the generalization hierarchyFragment of the generalization hierarchy
specialization
Main_airport Main_seaport
Sole_airport Sole_seaport
Strategically_essential_resource_or_infrastructure_element
Strategic_raw_material Strategically_essential_goods_or_materiel
War_materiel_and_transports
Raw_material
Strategically_essential_infrastructure_element
Resource_or_ infrastructure_element
<object>
Product
Non-strategically_essentialgoods_or_services
Farm-implementsof_Italy_1943
subconcept_of
instance_ofsubconcept_of
War_materiel_and_fuel
subconcept_of
UB
LB
+
+
_
G.Tecuci, Learning Agents Laboratory
Specialized ruleSpecialized rule
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O3
Plausible Upper Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
IFIdentify the strategic COG candidates with respect to the industrial civilization of a force
The force is ?O1
Plausible Upper Bound Condition?O1 IS Force
has_as_industrial_factor ?O2
?O2 IS Industrial_factor is_a_major_generator_of ?O3
?O3 IS Product
explanation?O1 has_as_industrial_factor ?O2?O2 is_a_major_generator_of ?O3
Plausible Upper Bound Condition?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2 IS Industrial_capacity is_a_major_generator_of ?O3
?O3 IS Strategically_essential_goods_or_materials
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
THENA strategic COG relevant factor is strategic COG candidate for a force
The force is ?O1The strategic COG relevant factor is ?O2
G.Tecuci, Learning Agents Laboratory
The rule refinement methodThe rule refinement method
General presentation of the rule refinement method
Rule refinement with a positive example
Rule refinement with a negative example
The rule refinement problem
Characterization of the learned PVS rule
G.Tecuci, Learning Agents Laboratory
Problem solving with PVS rulesProblem solving with PVS rules
PVS Condition Except-When PVS Condition
Rule not applicable
Rule’s conclusion
is (most likely)
incorrect
Rule’s conclusion is plausible Rule’s conclusion is
(most likely) correct
Rule’s conclusion is not plausible
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement in other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Agent teaching: Hands-on experience
G.Tecuci, Learning Agents Laboratory
Control of modeling, learning and solvingControl of modeling, learning and solving
Input Task
Generated Reduction
Mixed-Initiative Problem Solving
Ontology + Rules
Reject ReductionAccept ReductionNew Reduction
Rule Refinement
Task RefinementRule Refinement
Modeling
Formalization
Learning
Solution
G.Tecuci, Learning Agents Laboratory
Identify the strategic COG candidates for the Sicily_1943 scenario
Anglo_allies_1943
A systematic approach to agent teachingA systematic approach to agent teaching
European_Axis_1943
US_1943Britain_1943 Italy_1943Germany_1943
alliancealliance
individual states individual states1
2
35
other factors
other factors
4
16-19
20
controllingelement
governingelement
civilization
otherfactors
6
7
9
8
controllingelement
governingelement
civilization
otherfactors
10
11
12-15
G.Tecuci, Learning Agents Laboratory
Identify the strategic COG candidates for the Sicily_1943 scenario
Anglo_allies_1943
Identify the strategic COG candidates for Anglo_allies_1943
Which is an opposing force in the Sicily_1943 scenario?
Modeling, learning, problem solvingModeling, learning, problem solving
Is Anglo_allies_1943 a single member force or a multi-member force?
Anglo_allies_1943 is a multi-member force
Identify the strategic COG candidates for the Anglo_allies_1943which is a multi-member force
…
Rule_1
Rule_1 European_Axis_1943
Identify the strategic COG candidates for European_Axis_1943
Rule_2
Rule_2
Is European_Axis_1943 a single member force or multi-member force?
European_Axis_1943 is a multi-member force
Identify the strategic COG candidates for the European_Axis _1943which is a multi-member force
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement in other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Hands-on experience: Problem solving and learning
G.Tecuci, Learning Agents Laboratory
Agent teaching: hands-on experienceAgent teaching: hands-on experience
Problem Solvingand
Rule Refinement
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
Agent teaching: hands-on experienceAgent teaching: hands-on experience
AutonomousProblem Solving
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement in other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Hands-on experience: Problem solving and learning
G.Tecuci, Learning Agents Laboratory
Illustration of rule refinement in the COA domainIllustration of rule refinement in the COA domain
Rule refinement with a positive example:Minimal generalization of the plausible lower bound
Rule refinement with a negative example:Minimal specialization of the plausible upper bound
Rule refinement with a negative example:Adding an Except-When plausible version space condition
Integrated problem solving and learning
G.Tecuci, Learning Agents Laboratory
Rule: R2
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?O4 IS ALLEGIANCE-OF-UNIT
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1
Question: Is an enemy reconnaissance unit present?
Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action.
THEN accomplish the task:Assess-security-when-enemy-recon-is-present
for-coa ?O1for-unit ?O2for-recon-action ?O3
Ma
in
Co
nd
itio
n
Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
Plausible Lower Bound?O1 IS COA411?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN1?O4 IS RED--SIDE
Positive example that satisfies the upper bound
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa COA421
THEN accomplish the task:Assess-security-when-enemy-recon-is-present
for-coa COA421for-unit RED-CSOP2for-recon-action SCREEN2
Condition satisfied by positive example?O1 IS COA421?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN2
?O4 IS RED--SIDEle
ss g
ener
al t
han
A positive example covered by the upper boundA positive example covered by the upper bound
G.Tecuci, Learning Agents Laboratory
Plausible Lower Bound (from rule)?O1 IS COA411?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN1?O4 IS RED--SIDE
Plausible Lower Bound (from example)?O1 IS COA421?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN2?O4 IS RED--SIDE
New Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN-MILITARY-TASK?O4 IS RED--SIDE
minimal generalization
SCREEN1
SCREEN-MILITARY-TASK
INSTANCE-OF
SCREEN2
INSTANCE-OF
INTELLIGENCE-COLLECTION-MILTARY-TASK
SUBCLASS-OF
COA411
INSTANCE-OF
COA421
INSTANCE-OF
COA-SPECIFICATION-MICROTHEORY
Minimal generalization of the plausible lower boundMinimal generalization of the plausible lower bound
G.Tecuci, Learning Agents Laboratory
Generalized ruleGeneralized rule
Rule: R2
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?O4 IS ALLEGIANCE-OF-UNIT
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1
Question: Is an enemy reconnaissance unit present?
Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action.
THEN accomplish the task:Assess-security-when-enemy-recon-is-present
for-coa ?O1for-unit ?O2for-recon-action ?O3
Ma
in
Co
nd
itio
n
Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
Plausible Lower Bound?O1 IS COA411?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN1?O4 IS RED--SIDE
Rule: R2
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?O4 IS ALLEGIANCE-OF-UNIT
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1
Question: Is an enemy reconnaissance unit present?
Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action.
THEN accomplish the task:Assess-security-when-enemy-recon-is-present
for-coa ?O1for-unit ?O2for-recon-action ?O3
Ma
in
Co
nd
itio
n
Explanation: ?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 TASK ?O3?O3 IS SCREEN-MILITARY-TASK?O4 IS RED--SIDE
G.Tecuci, Learning Agents Laboratory
Illustration of rule refinement in the COA domainIllustration of rule refinement in the COA domain
Rule refinement with a positive example:Minimal generalization of the plausible lower bound
Rule refinement with a negative example:Minimal specialization of the plausible upper bound
Rule refinement with a negative example:Adding an Except-When plausible version space condition
Integrated problem solving and learning
G.Tecuci, Learning Agents Laboratory
Rule: R$ASWCER-001IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1
Question: Is an enemy reconnaissance unit present?
Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action.
Explanation:•?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE•?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
THEN accomplish the task:Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3
Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS SCREEN—MILITARY-TASK?O4 IS RED--SIDE
Ma
in C
on
dit
ion
Negative example that satisfies the upper bound
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa COA51
THEN accomplish the task:Assess-security-when-enemy-recon-is-present for-coa COA51 for-unit BLUE-BATTALION1 for-recon-action SCREEN-RIGHT
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK
?O4 IS ALLEGIANCE-OF-UNIT
Condition satisfied by positive example?O1 IS COA51
?O2 IS BLUE-BATTALION1
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS SCREEN-RIGHT
?O4 IS BLUE-SIDE
less
gen
eral
th
an
A negative example covered by the upper boundA negative example covered by the upper bound
G.Tecuci, Learning Agents Laboratory
RED-SIDEBLUE-SIDE
ALLEGIANCE-OF-UNIT
SUBCLASS-OF
_
specialization
?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK?O4 IS ALLEGIANCE-OF-UNIT
?O1 IS COA51
?O2 IS BLUE-BATALLION1
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS SCREEN-RIGHT?O4 IS BLUE-SIDE
?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS SCREEN--MILITARY TASK?O4 IS RED-SIDE
Negative Example Specialized Plausible Upper Bound
Plausible Upper Bound (from rule)
specialization
Minimal specialization of the plausible upper boundMinimal specialization of the plausible upper bound
G.Tecuci, Learning Agents Laboratory
Rule: R$ASWCER-001
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK?O4 IS ALLEGIANCE-OF-UNIT
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa ?O1
Question: Is an enemy reconnaissance unit present?
Answer: Yes, the enemy unit ?O2 is performing the action ?O3 which is a reconnaissance action.
Explanation:•?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE•?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
THEN accomplish the task:Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3
Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MECHANIZED-INFANTRY-UNIT--MIL-SPECIALTY
SOVEREIGN-ALLEGIANCE-OF-ORG ?O4
TASK ?O3
?O3 IS SCREEN--MILITARY TASK?O4 IS RED--SIDE
Ma
in C
on
dit
ion
Rule specializationRule specialization
Negative example that satisfies the upper bound
IF the task to accomplish is:Assess-security-wrt-countering-enemy-reconnaissance for-coa COA51
THEN accomplish the task:Assess-security-when-enemy-recon-is-present for-coa COA51 for-unit BLUE-BATTALION1 for-recon-action SCREEN-RIGHT
Failure Explanation:•BLUE-SIDE is ALLEGIANCE-OF-UNIT but is not RED-SIDE
Explanation:• BLUE-BATTALION1 SOVEREIGN-ALLEGIANCE-OF-ORG
BLUE-SIDE• BLUE-BATTALION1 TASK SCREEN-RIGHT • SCREEN-RIGHT IS INTELLIGENCE-COLLECTION--MIL-TASK
The above reduction is incorrectin spite of
Because
RED--SIDE
minimal specialization
G.Tecuci, Learning Agents Laboratory
Illustration of rule refinement in the COA domainIllustration of rule refinement in the COA domain
Rule refinement with a positive example:Minimal generalization of the plausible lower bound
Rule refinement with a negative example:Minimal specialization of the plausible upper bound
Rule refinement with a negative example:Adding an Except-When plausible version space condition
Integrated problem solving and learning
G.Tecuci, Learning Agents Laboratory
Generation of the failure explanationGeneration of the failure explanation
Rule: R$ASWERIP-002
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?S1 IS “HIGH”
Explanation:•?S1 IS ALWAYS “HIGH”
IF the task to accomplish is:Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3
Question: Is the enemy unit destroyed?
Answer: No, ?O2 is not countered
Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?S1 IS “HIGH”
THEN accomplish the task:Report-weakness-in-security-because-enemy-recon-is-not-countered for-coa ?O1 for-unit ?O2 for-recon-action ?O3 with-importance ?S1
Ma
in
Co
nd
itio
n
Negative ex. that satisfies the upper bound
IF the task to accomplish is:Assess-security-when-enemy-recon-is-present for-coa COA411 for-unit RED-CSOP1 for-recon-action SCREEN1
THEN accomplish the task:Report-weakness-in-security-because-enemy-recon-is-not-countered for-coa COA411 for-unit RED-CSOP1 for-recon-action SCREEN1 with-importance High
Failure Explanation:• DESTROY1 OBJECT-ACTED-ON RED-CSOP1• DESTROY1 IS DESTROY-MILITARY-TASK
The above reduction is incorrectBecause enemy recon is countered
G.Tecuci, Learning Agents Laboratory
Rule refinement withthe failure explanationRule refinement withthe failure explanation
Rule: R$ASWERIP-002
Plausible Upper Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?S1 IS “HIGH”
Explanation:•?S1 IS ALWAYS “HIGH”
IF the task to accomplish is:Assess-security-when-enemy-recon-is-present for-coa ?O1 for-unit ?O2 for-recon-action ?O3
Question: Is the enemy unit destroyed?
Answer: No, ?O2 is not countered
Plausible Lower Bound?O1 IS COA-SPECIFICATION-MICROTHEORY
?O2 IS MODERN-MILITARY-UNIT--DEPLOYABLE
?O3 IS INTELLIGENCE-COLLECTION--MILITARY-TASK?S1 IS “HIGH”
THEN accomplish the task:Report-weakness-in-security-because-enemy-recon-is-not-countered for-coa ?O1 for-unit ?O2 for-recon-action ?O3 with-importance ?S1
Failure Explanation:•?O4 OBJECT-ACTED-ON ?O2 •?O4 IS DESTROY-MILITARY-TASK
Plausible Upper Bound?O4 IS DESTROY-MILITARY-TASK OBJECT-ACTED-ON ?O2
Plausible Lower Bound?O4 IS DESTROY1 OBJECT-ACTED-ON ?O2
Ma
in
Co
nd
itio
nE
xc
ep
t W
he
n
Co
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itio
n
Both bounds are specialized with an Except When condition
G.Tecuci, Learning Agents Laboratory
Illustration of rule refinement in the COA domainIllustration of rule refinement in the COA domain
Rule refinement with a positive example:Minimal generalization of the plausible lower bound
Rule refinement with a negative example:Minimal specialization of the plausible upper bound
Rule refinement with a negative example:Adding an Except-When plausible version space condition
Integrated problem solving and learning
G.Tecuci, Learning Agents Laboratory
Assess COA wrt Principle of Securityfor-coa COA411
R$
AS
WE
RIP
-0
02
RuleLearning
Does the COA include security and counter-recon actions, a security element, a rear element, and identify risks?
Assess security wrt countering enemy reconnaissancefor-coa COA411
I consider enemy reconnaissance
R$
AS
WC
ER
-00
1
RuleLearning
R$
AC
WP
OS
-00
1
RuleLearning
Is an enemy reconnaissance unit present?
Assess security when enemy recon is presentfor-coa COA411for-unit RED-CSOP1for-recon-action SCREEN1
Yes, RED-CSOP1 which is performingthe reconnaissance action SCREEN1
Yes, RED-CSOP1 is destroyed by DESTROY1
Is the enemy reconnaissance unit destroyed?
Report strength in security because of countering enemy reconfor-coa COA411for-unit RED-CSOP1for-recon-action SCREEN1for-action DESTROY1with-importance “high”
G.Tecuci, Learning Agents Laboratory
Assess COA wrt Principle of Securityfor-coa COA421
Does the COA include security and counter-recon actions, a security element, a rear element, and identify risks?
Assess security wrt countering enemy reconnaissancefor-coa COA421
I consider enemy reconnaissance
Is an enemy reconnaissance unit present?
Assess security when enemy recon is presentfor-coa COA421for-unit RED-CSOP2for-recon-action SCREEN2
Yes, RED-CSOP2 which is performingthe reconnaissance action SCREEN2
No
Is the enemy reconnaissance unit destroyed?
Report weakness in security because enemy recon is not counteredfor-coa COA421for-unit RED-CSOP2for-recon-action SCREEN2with-importance “high”
RuleRefinement
R$
AC
WP
OS
-00
1
R$
AS
WC
ER
-00
1
RuleRefinement
R$
AS
WE
RIP
-0
02
RuleLearning
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement in other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Hands-on experience: Problem solving and learning
G.Tecuci, Learning Agents Laboratory
Illustration of rule refinement in other domainsIllustration of rule refinement in other domains
Illustration in the assessment and tutoring domain
Illustration in the manufacturing domain
G.Tecuci, Learning Agents Laboratory
Illustration in the manufacturing domainIllustration in the manufacturing domain
G. Tecuci, Building Intelligent Agents, Academic Press, 1998, pp. 21-23, pp. 101-129 (required reading).
See:
G.Tecuci, Learning Agents Laboratory
Illustration in the assessment and tutoring domainIllustration in the assessment and tutoring domain
G. Tecuci, Building Intelligent Agents, Academic Press, 1998, pp. 27-32, pp. 198-228 (required reading).
See:
G.Tecuci, Learning Agents Laboratory
OverviewOverview
Integrated modeling, learning, and solving
Illustration of rule refinement in the COA domain
Illustration of rule refinement other domains
Required reading
The rule refinement method
Characterization of the PVS learning method
Hands-on experience: Problem solving and learning
G.Tecuci, Learning Agents Laboratory
Characterization of the PVS ruleCharacterization of the PVS rule
G.Tecuci, Learning Agents Laboratory
Characterization of the rule learning methodCharacterization of the rule learning method
Uses the explanation of the first positive example to generate a much smaller version space than the classical version space method.
Conducts an efficient heuristic search of the version space, guided by explanations, and by the maintenance of a single upper bound condition and a single lower bound condition.
Will always learn a rule, even in the presence of exceptions.
Learns from a few examples and an incomplete knowledge base.
Uses a form of multistrategy learning that synergistically integrates learning from examples, learning from explanations, and learning by analogy, to compensate for the incomplete knowledge.
Uses mixed-initiative reasoning to involve the expert in the learning process.
Is applicable in complex real-world domains, being able to learn within a complex representation language.
G.Tecuci, Learning Agents Laboratory
Required readingRequired reading
G. Tecuci, Building Intelligent Agents, Academic Press, 1998, pp. 21-23, pp. 27-32, pp. 101-129, pp. 198-228 (required).
Tecuci G., Boicu M., Bowman M., and Dorin Marcu, with a commentary by Murray Burke,“An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a High Performance Knowledge Base for Course of Action Critiquing,” invited paper for the special IAAI issue of the AI Magazine, Volume 22, No, 2, Summer 2001, pp. 43-61.http://lalab.gmu.edu/publications/data/2001/COA-critiquer.pdf (required).
Boicu M., Tecuci G., Stanescu B., Marcu D. and Cascaval C., "Automatic Knowledge Acquisition from Subject Matter Experts," in Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, Dallas, Texas, November 2001. http://lalab.gmu.edu/publications/data/2001/ICTAI.doc (required).