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Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci [email protected] http://lalab.gmu.e du/ CS 785, Fall 2001

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Page 1: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 2: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 3: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 4: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 5: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 6: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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)

Page 7: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 8: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 9: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 10: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 11: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 12: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 13: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 14: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 15: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 16: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 17: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 18: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 19: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

G.Tecuci, Learning Agents Laboratory

S3.Consider the following description:

?z IS cleanerREMOVES surplus-paint

Determine all the possible values of ?z.

ExerciseExercise

Page 20: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 21: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 22: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 23: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 24: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 25: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 26: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 27: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 28: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 29: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 30: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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.

Page 31: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 32: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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:

Page 33: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 34: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 35: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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>

Page 36: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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

Page 37: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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):

Page 38: G.Tecuci, Learning Agents Laboratory Learning Agents Laboratory Department of Computer Science George Mason University Gheorghe Tecuci tecuci@cs.gmu.edu

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):

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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.

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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

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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

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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

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G.Tecuci, Learning Agents Laboratory

END

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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

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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:

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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

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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

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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