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
Define the problem reduction approach to problem solving.
What is an instance?
What is a concept?
What is a positive example of a concept?
What is a negative example of a concept?
Give an intuitive definition of generalization.
What does it mean for concept A to be more general than concept B?
Indicate a simple way to prove that a concept is not more general than another concept.
Given two concepts C1 and C2, from a generalization point of view, what are all the different possible relations between them?
What are the basic elements in the definition of a property or a relation?
Briefly define a plausible version space rule.
Sample questionsSample questions
G.Tecuci, Learning Agents Laboratory
What is a generalization rule?
What is a specialization rule?
What is a reformulation rule?
Name all the generalization rules you know.
Briefly describe and illustrate with an example the “turning constants into variables” generalization rule.
Define and illustrate the dropping conditions generalization rule.
Define the following:• a generalization of two concepts• a minimally general generalization of two concepts• the least general generalization of two concepts• the maximally general specialization of two concepts.
Define the transitivity of ISA.
Define the inheritance of features (including default inheritance and multiple inheritance).
Sample questionsSample questions
G.Tecuci, Learning Agents Laboratory
Briefly explain the process of reasoning with a plausible version space rule.
Define the rule learning problem in Disciple.
Briefly describe the rule learning method of Disciple.
What is an explanation of an example?
Briefly describe analogical reasoning (in general).
Briefly describe analogical reasoning in Disciple.
Define the rule refinement problem in Disciple.
Briefly describe the rule refinement method of Disciple.
What is a negative exception?
What is a positive exception?
Draw a picture representing a plausible version space, as well as a positive example, a negative example, a positive exception and a negative exception. Then briefly define each of these elements.
Describe briefly the general architecture of the Disciple shell and the methodology for building a Disciple agent.
Sample questionsSample questions
G.Tecuci, Learning Agents Laboratory
Consider the cells consisting of two bodies, each body having two attributes: - color (that may be yellow or green) and - number of nuclei (1 or 2). The relative position of the bodies is not relevant because they can move inside the cell.
((1 green) (2 yellow))+
a) Indicate ALL the possible generalizations of the following cell, and the generalization relations between them.
b) Determine the number of the distinct sets of instances and the number of concept descriptions for this problem.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
c) Given the following cell descriptions
((1 green) (1 green)) ((1 green) (2 green))((1 yellow) (2 green))
Determine the following minimal generalizations:g(E1, E2), g(E2, E3), g(E3, E1), g(E1, E2, E3)
G.Tecuci, Learning Agents Laboratory
black
...
...
CONTAINS
STATE fluid
INSTANCE-OF
GLUES
MADE-OF
PART-OF
COLOR
PROVIDER
SOMETHING
INFLAMMABLE-OBJECT
CAOUTCHOUCGLUE-INC CONTACT-ADHESIVE1 CHASSIS-ASSEMBLY1
MOWICOLL1
MOWICOLL
ADHESIVE
TOXIC-SUBSTANCE
LOUDSPEAKER
LOUDSPEAKER-COMPONENTFRAGIL-OBJECTMATERIAL
CONTACT-ADHESIVE
PAPER
MEMBRANE
BOLT1
CHASSIS-ASSEMBLY
MEMBRANE1
MECHANICAL-CHASSIS
MECHANICAL-CHASSIS1METAL
BOLT
CHASSIS-MEMBRANE-ASSEMBLY
CHASSIS-MEMBRANE-ASSEMBLY1
ISA
ISAISA
ISAISA
ISAISA
ISAISA ISA ISA
ISA
ISAISA ISA
ISA
ISA
ISA
ISA
ISA
ISA
GLUES
GLUES
GLUES
GLUES
GLUES
PROVIDER
INSTANCE-OF
INSTANCE-OF
INSTANCE-OF INSTANCE-OF
INSTANCE-OF
INSTANCE-OF
MADE-OF
MADE-OF
MADE-OF
PART-OF
PART-OF
...
The following exercises use the background knowledge consisting of this object hierarchy (semantic network) and the feature definitions from the next slide.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
name description domain range
IS is SOMETHING SOMETHING
OBJECT object TASK SOMETHING
TO to TASK SOMETHING
MADE-OF made of SOMETHING MATERIAL
GLUES glues ADHESIVE MATERIAL
STATE state SOMETHING {solid fluid gas}
TASK task OPERATION TASK
INTO into OPERATION TASK
ON on TASK SOMETHING
PART-OF part of SOMETHING SOMETHING
Feature Definitions
G.Tecuci, Learning Agents Laboratory
Consider the question:
“Is there a part of a loudspeaker that is made of metal?”
a) Which are all the answers to this question?
b) Which are the reasoning operations that need to be performed in order to answer this question.
c) Consider one of the answers that requires all these operations and show how the answer is found.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Consider the following expressions:
E1: ?X IS MEMBRANE E2: ?X IS MECHANICAL-CHASSIS MADE-OF ?M MADE-OF ?M
?M IS PAPER ?M IS METAL ?Z IS CONTACT-ADHESIVE ?Z IS MOWICOLL
GLUES ?M GLUES ?M STATE fluid
a) Find the minimally general generalizations of E1 and E2.
b) Find two generalizations of E1 and E2 that are not minimally general generalizations.
c) Consider one of the generalizations found at b) and demonstrate why it is a generalization of E1 and E2 but it is not a minimally general generalization.
d) What would be a least general generalization of E1 and E2? Does it exist?
e) Indicate a specialization of E1.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Construct the plausible version space rule learned from them.
IF the task to accomplish isATTACH OBJECT MEMBRANE1 TO CHASSIS-ASSEMBLY1
THEN accomplish the tasksAPPLY OBJECT CONTACT-ADHESIVE1 ON CHASSIS-ASSEMBLY1PRESS OBJECT MEMBRANE1 ON CHASSIS-ASSEMBLY1
CONTACT-ADHESIVE1 IS fluidCONTACT-ADHESIVE1 GLUES PAPER and MEMBRANE1 MADE-OF PAPERCONTACT-ADHESIVE1 GLUES METAL and CHASSIS-ASSEMBLY1 MADE-OF METAL
Consider the following example and its explanation:
Because
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Compose an example analogous with the following one:
explains
IF the task is ATTACH OBJECT MEMBRANE1 TO CHASSIS-ASSEMBLY1THEN decompose this task into the subtasks APPLY OBJECT CONTACT-ADHESIVE1 ON MEMBRANE1 PRESS OBJECT MEMBRANE1 ON CHASSIS-ASSEMBLY1
STATEfluidCONTACT-ADHESIVE1
METAL
PAPER
MEMBRANE1
CHASSIS-ASSEMBLY1
MADE-OF
GLUES
MADE-OF
GLUES
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Find a minimal generalization of the rule that covers the positive
example.
IF the task to accomplish isATTACH OBJECT ?X TO ?Y
Plausible Upper Bound IF?X IS SOMETHING
MADE-OF ?M?Y IS SOMETHING
MADE-OF ?N?Z IS ADHESIVE
GLUES ?MGLUES ?N
?M IS MATERIAL?N IS MATERIAL
Plausible Lower Bound IF?X IS MEMBRANE1
MADE-OF ?M?Y IS CHASSIS-ASSEMBLY1
MADE-OF ?N?Z IS CONTACT-ADHESIVE1
GLUES ?MGLUES ?N
?M IS PAPER?N IS METAL
THEN accomplish the tasksAPPLY OBJECT ?Z ON ?XPRESS OBJECT ?X ON ?Y
IF the task to accomplish isATTACH OBJECT BOLT1 TO MECHANICAL-CHASSIS1
THEN accomplish the tasksAPPLY OBJECT MOWICOLL1 ON MECHANICAL-CHASSIS1PRESS OBJECT BOLT1 ON MECHANICAL-CHASSIS1
Rule
Positive Example
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Find a minimal specialization of the rule that does not cover the positive example:•By using an additional explanation of the positive examples;
•By empirically specializing the rule.
IF the task to accomplish isATTACH OBJECT ?X TO ?Y
Plausible Upper Bound IF?X IS SOMETHING
MADE-OF ?M?Y IS SOMETHING
MADE-OF ?N?Z IS ADHESIVE
GLUES ?MGLUES ?N
?M IS MATERIAL?N IS MATERIAL
Plausible Lower Bound IF?X IS MEMBRANE1
MADE-OF ?M?Y IS LOUDSPEAKER-COMPONENT
MADE-OF ?N?Z IS LOUDSPEAKER-COMPONENT
GLUES ?MGLUES ?N
?M IS MATERIAL?N IS METAL
THEN accomplish the tasksAPPLY OBJECT ?Z ON ?XPRESS OBJECT ?X ON ?Y
with the positive examples(?X IS MEMBRANE1, ?Y IS CHASSIS-ASSEMBLY1, ?Z IS CONTACT-ADHESIVE1, ?M IS PAPER, ?N IS METAL)
(?X IS BOLT1, ?Y IS MECHANICAL-CHASSIS1,?Z IS MOWICOLL1, ?M IS METAL, ?N IS METAL)
IF the task to accomplish isATTACH OBJECT SCREENING-CAP1 TO LOUDSPEAKER1
THEN accomplish the tasksAPPLY OBJECT SCOTCH-TAPE1 ON SCREENING-CAP1PRESS OBJECT SCREENING-CAP1 ON LOUDSPEAKER1
Rule
Negative Example
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Explain how the following questions are answered, and provide the corresponding answer(s):
What is the color of membrane?
What does contact-adhesive1 glue?
Is there a loudspeaker component made of metal?
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
The following exercises, marked S1 to S7, are based on the following semantic network from the loudspeaker manufacturing domain:
AIR-MOVER
SOFT-CLEANER
DUST
AIR-JET-DEVICE SOLVENT
AIR-SUCKER ACETONE ALCOHOLAIR-PRESS
HARD-CLEANER
CLEANER LOUDSPEAKER-COMPONENT
WASTE-MATERIAL
EMERY-PAPER
ENTREFER MEMBRANE
SURPLUS-ADHESIVE SURPLUS-PAINT
SOMETHING
REMOVESREMOVES
REMOVES
DAMAGES
MAY-HAVE MAY-HAVE
Remark: Consider that each most specific concept, such as DUST or AIR-PRESS, has an instance, such as DUST1 or AIR-PRESS1.
ExercisesExercises
G.Tecuci, Learning Agents Laboratory
S1. Consider the following two expressions:
E1: ?X IS SOFT-CLEANERREMOVES ?Z
?Y IS MEMBRANEMADE-OF ?T
?Z IS WASTE-MATERIAL
E2: ?X IS AIR-SUCKERREMOVES ?ZNOT-DAMAGES PAPER
?Y IS MEMBRANEMADE-OF PAPER
?Z IS DUST
Use the generalization rules to show that E1 is more general than E2.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
S2.Determine the generalization of the following two expressions:
E1: ?x IS entreferMAY-HAVE ?y
?y IS dust?z IS air-sucker
REMOVES ?y
E2: ?x IS membraneMAY-HAVE ?y
?y IS surplus-adhesive?z IS alcohol
TYPE fluidREMOVES ?y
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
S3.Consider the following description:
?z IS cleanerREMOVES surplus-paint
Determine all the possible values of ?z.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
S4.Consider the following action description:
CLEAN OBJECT ?xOF ?yWITH ?z
Condition?x IS entrefer
MAY-HAVE ?y?y IS something?z IS cleaner
REMOVES ?y
Find all the possible values for the variables ?x, ?y and ?z.Indicate some of the corresponding actions.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
S5.Consider the following rule:
IF the task to perform isCLEAN OBJECT ?x OF ?y
Condition?x IS something
MAY-HAVE ?y?y IS something?z IS cleaner
REMOVES ?y
THEN perform the taskCLEAN OBJECT ?x OF ?y WITH ?z
Describe how this rule is applied to solve the problem:CLEAN OBJECT entrefer1 OF dust1
Which will be the result?
Remark: Consider that each most specific concept o from the object ontology has an instance o1.
G.Tecuci, Learning Agents Laboratory
IF the task to perform isCLEAN OBJECT ?x OF ?y
Condition?x IS something
MAY-HAVE ?y?y IS something?z IS cleaner
REMOVES ?y
THEN perform the taskCLEAN OBJECT ?x OF ?y WITH ?z
Describe how this rule is applied to solve the problem:CLEAN OBJECT membrane1 OF surplus-adhesive1
Which will be the result?
Remark: Consider that each most specific concept o from the object ontology has an instance o1.
S6. Consider the following rule:
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
IF the task to perform isCLEAN OBJECT ?x OF ?y
G: plausible upper bound?x IS something
MAY-HAVE ?y?y IS something?z IS something
REMOVES ?y
S: plausible lower bound?x IS entrefer
MAY-HAVE ?y?y IS dust?z IS air-sucker
REMOVES ?y
THEN perform the taskCLEAN OBJECT ?x OF ?y WITH ?z
S7.Consider the following partially learned rule:
Describe how Disciple generalizes this rule so as to cover the following positive example:
IF the task to perform isCLEAN OBJECT membrane1 OF surplus-adhesive1THEN perform the taskCLEAN OBJECT membrane OF surplus-adhesive1 WITH alcohol1
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Develop an object ontology that represents the following information:
Puss is a calico.Herb is a tuna.Charlie is a tuna.All tunas are fishes.All calicos are cats.Cats like to eat fishes.
You should define object concepts, object features and instances.
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Develop an object ontology that represents the following information:
The color of Apple1 is red.The color of Apple2 is green.Apple1 is an apple.Apple2 is an apple.Apples are fruits.
You should define object concepts, object features and instances.
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Develop an object ontology that represents the following information:
Basketball players are tall. Muresan is a basketball player. Muresan is tall.
You should define object concepts, object features and instances.
G.Tecuci, Learning Agents Laboratory
Insert the additional knowledge that platypus lays eggs into the following object ontology:
ExerciseExercise
mammal
cow platypus
birth-mode livesubclass-ofsubclass-of
Explain the result.
G.Tecuci, Learning Agents Laboratory
Develop an object ontology that represents the following information:
"Blue task force 1 penetrates Red mechanized brigade 1 with a force ratio of 10.6. The recommended force ratio for a penetration is 3. A penetration is a complex military task, a military maneuver and a military attack. Use of a penetration indicates that the mission is offensive“
You should draw the ontology and should also define the features used in it (in terms of their domains and ranges).
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
Develop an object ontology that represents the following information:
"BLUE-TASK-FORCE1 is a blue armored and mechanized infantry battalion assigned to be main effort1. It performs two tasks, penetrate1 and clear1. It has a regular strength and has the following units under its operational control: BLUE-MECH-COMPANY1, BLUE-MECH-COMPANY2, BLUE-ARMOR-COMPANY1, BLUE-ARMOR-COMPANY2”
You should draw the ontology and should also define the features used in it (in terms of their domains and ranges).
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Consider the background knowledge represented by the following generalization hierarchies:
any-color
warm-color cold-color
red yelloworange blackblue green
any-shape
polygone round
triangle rectangle
square
circle ellipse
Consider also the following concept:
E: ?u IS objectCOLOR yellowSHAPE circleRADIUS 5
Indicate five different generalization rules. For each such rule determine an expression Eg which is more general than E according to that rule.
G.Tecuci, Learning Agents Laboratory
I need to
Identify and test a strategic COG candidate for Okinawa_1945 which is a major theater of war scenario
US_1945
Therefore I need to
Which is an opposing force in the Okinawa_1945 scenario?
Identify and test a strategic COG candidate for US_1945
Is US_1945 a single-member force or a multi-member force?
US_1945 is a single-member force
Identify and test a strategic COG candidate for US_1945 which is a single-member force
Therefore I need to
Formalize the following tasks:
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
US_1943 has_as_industrial_factor
Industrial_capacity_of_US_1943
Identify the strategic COG candidates with respect to the industrial civilization of a force The force is US_1943
A strategic COG relevant factor is strategic COG candidate for a force
The force is US_1943The strategic COG relevant factor is
Industrial_capacity_of_US_1943
IF the task to accomplish is
THEN
explains
War_materiel_and_transports_of_US_1943
is_a_major_generator_of
a) Find the analogy-based generalization of the explanations and the example.
b) Find the plausible version space rule that will be learned from this example.
Consider the following problem solving episode and its explanation, in the context of the background knowledge the following four slides:
G.Tecuci, Learning Agents Laboratory
Feature definitionsFeature definitions
has_as_industrial_factorD: ForceR: Industrial_factor
is_a_major_generator_ofD: Economic_factorR: Product
The force isD: taskR: Force
The strategic COG relevant factor isD: taskR: Force
G.Tecuci, Learning Agents Laboratory
Economic factorsEconomic factors
Economic_factor
Other_economic_
factor
Transportation_Network_or_system
Industrial_authority
Commerce_authority
Industrial_Capacity
Industrial_Center
Strategic_Raw_
Material
Transportation_Center
Information_Network_or_system
Transportation_Factor
Industrial_factor
Germany_1943
has_as_strategic_raw_material
Oil_chromium_copper_and_bauxite_
of_Germany_1943
is_obtained_from
is_critical_to_the_production_of
Balkans
War_materiel_of_Germany_1943
Raw_material
US_1943
is_a_major_generator_of
war_materiel_and_ transports_of_
US_1943_
has_as_industrial_factor
industrial_capacity_of_US_1943
Farm_implement_industry_of_Italy_1943
Farm_implement_industry
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
Fragment of the generalization hierarchyFragment of the generalization hierarchy
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
War_materiel_and_fuel
Resource
Farm-implements
War_materiel_and_fuel_of_Germany_1943
War_materiel_and_transports_of_US_1943
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
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
Minimally generalize the rule to cover the following positive example (considering the background knowledge from the previous four slides):
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
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
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
Minimally specialize the rule to no longer cover the following negative example (considering the background knowledge from the previous slides):
G.Tecuci, Learning Agents Laboratory
Repertory grid exercisesRepertory grid exercises
Define a repertory grid for choosing a course to enroll in.
Define a repertory grid for choosing a car.
Define a repertory grid for choosing a dissertation director.
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Consider the following two concepts:
Indicate different generalization of them.
C 1: ?X IS SCREW
HEAD HEXAGONAL COST 5
C 2: ?X IS NUT
COST 6
G.Tecuci, Learning Agents Laboratory
ExerciseExercise
Consider the following two concepts and ontology. Indicate four specializations of G1 and G2 (including a maximally general specialization).
?X IS LOUDSPEAKER-COMPONENTMADE-OF ?M
?M IS MATERIAL
?Z IS ADHESIVEGLUES ?M
G1: ?X IS LOUDSPEAKER-COMPONENTMADE-OF ?M
?M IS MATERIAL
?Z IS INFLAMMABLE-OBJECTGLUES ?M
G2:
LOUDSPEAKER-COMPONENT
MEMBRANE CHASSIS-ASSEMBLY BOLT
ADHESIVE INFLAMMABLE-OBJECTTOXIC-SUBSTANCE
SCOTCH-TAPE SUPER-GLUE MOWICOLL CONTACT-ADHESIVE
MATERIAL
CAOUTCHOUC PAPER METAL
IS ISIS IS IS
IS
IS
IS
ISISIS
ISISIS
G.Tecuci, Learning Agents Laboratory
Develop an object ontology that represents the following information:
Birds have feathers, fly and lay eggs.Albatros is a bird.Donald is a bird. Tracy is an albatros.
You should define object concepts, object features and instances.
ExerciseExercise
G.Tecuci, Learning Agents Laboratory
END
G.Tecuci, Learning Agents Laboratory
Cooperative problem solving and learningCooperative problem solving and learning
Problem solving with PVS rules
Integrated problem solving and learning
Demonstration
G.Tecuci, Learning Agents Laboratory
Generalization by analogyGeneralization by analogy
TASKRED-CSOP1 SCREEN1
SOVEREIGN-ALLEGIANCE-OF-ORG RED--SIDE
INTELLIGENCE-COLLECTION-MILTARY-TASK
INSTANCE-OF
Assess security wrt countering enemy reconnaissancefor-coa COA411
Assess security when enemy recon is presentfor-coa COA411for-unit RED-CSOP1for-recon-action SCREEN1
IF the task to accomplish is:
THEN accomplish the task:
explain generalization
Any value of ?O2 should be an instance of:DOMAIN(TASK) DOMAIN(SOVEREIGN-ALLENGINCE-OF_ORG) RANGE(FOR-UNIT) = MODERN-MILITARY-UNIT--DEPLOYABLE
Any value of ?O3 should be an instance of:RANGE(TASK) INTELLIGENCE-COLLECTION-MILITARY-TASK = INTELLIGENCE-COLLECTION-MILITARY-TASK
Any value of ?O4 should be an instance of:RANGE(SOVEREIGN-ALLENGINCE-OF_ORG) = ALLEGIANCE-OF-UNIT
Any value of ?O1 should be an instance of:RANGE(FOR-COA) = COA-SPECIFICATION-MICROTHEORY
Knowledge-base constraints on the generalization:
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
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
SCREEN1
SCREEN-MILITARY-TASK
INSTANCE-OF
SCREEN2
INSTANCE-OF
INTELLIGENCE-COLLECTION-MILTARY-TASK
SUBCLASS-OF
COA411
INSTANCE-OF
COA421
INSTANCE-OF
COA-SPECIFICATION-MICROTHEORY