CS 785, Fall 2001
Gheorghe Tecuci
tecuci@cs.gmu.edu
http://lalab.gmu.edu/
Learning Agents Laboratory
Department of Computer Science
George Mason University
 G.Tecuci, Learning Agents Laboratory
Sample questions
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.
 G.Tecuci, Learning Agents Laboratory
Sample questions
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).
 G.Tecuci, Learning Agents Laboratory
Sample questions
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.
 G.Tecuci,
Learning Agents Laboratory
Exercise
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.
a) Indicate ALL the possible generalizations of the following cell,
and the generalization relations between them.
+
((1 green) (2 yellow))
b) Determine the number of the distinct sets of instances and the
number of concept descriptions for this problem.
 G.Tecuci, Learning Agents Laboratory
c) Given the following cell descriptions
((1 green) (1 green))
((1 yellow) (2 green))
((1 green) (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
Exercise
The following exercises use the background knowledge consisting of this
object hierarchy (semantic network) and the feature definitions from the
next slide.
SOMETHING
ISA
INFLAMMABLE
-OBJECT
...
ISA
ISA
ISA
ADHESIVE
...
MATERIAL
FRAGIL
-OBJECT
TOXIC-SUBSTANCE
ISA
ISA
ISA
ISA
PAPER
GLUES
GLUES
STATE
MOWICOLL
fluid CONTACT
-ADHESIVE ISA
PROVIDER
GLUES
GLUES
GLUES
...
INSTANCE
-OF
PROVIDER
GLUES
MOWICOLL
1
METAL
ISA
MADE-OF MEMBRANE
-OF
COLOR INSTANCE
MEMBRANE
1
black
PART-OF
ISA
MECHANICAL
-CHASSIS
MADE-OF INSTANCE
-OF
MECHANICAL
-CHASSIS
1
MADE-OF
INSTANCE
-OF
 G.Tecuci, Learning Agents Laboratory
ISA
CHASSIS
MEMBRANE
ASSEMBLY
ISA
INSTANCE
-OF
CONTAINS
ISA
CHASSIS
MEMBRANE
ASSEMBLY
1
MADE-OF
ISA
GLUE-INC
PART-OF
LOUDSPEAKER
-COMPONENT
ISA
ISA
ISA
LOUDSPEAKER
ISA
ISA
ISA
CAOUTCHOUC CONTACT
-ADHESIVE
1
CHASSIS
-ASSEMBLY
PART-OF
INSTANCE
-OF
CHASSIS
-ASSEMBLY
1
BOLT
INSTANCE
-OF
BOLT1
Feature Definitions
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
 G.Tecuci, Learning Agents Laboratory
Exercise
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.
 G.Tecuci, Learning Agents Laboratory
Exercise
Consider the following expressions:
E1: ?X IS MEMBRANE
MADE-OF ?M
?M IS PAPER
?Z IS CONTACT-ADHESIVE
GLUES ?M
STATE fluid
E2: ?X IS MECHANICAL-CHASSIS
MADE-OF ?M
?M IS METAL
?Z IS MOWICOLL
GLUES ?M
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.
 G.Tecuci, Learning Agents Laboratory
Exercise
Consider the following example and its explanation:
IF the task to accomplish is
ATTACH OBJECT MEMBRANE1 TO CHASSIS-ASSEMBLY1
THEN accomplish the tasks
APPLY OBJECT CONTACT-ADHESIVE1 ON CHASSIS-ASSEMBLY1
PRESS OBJECT MEMBRANE1 ON CHASSIS-ASSEMBLY1
Because
CONTACT-ADHESIVE1 IS fluid
CONTACT-ADHESIVE1 GLUES PAPER and MEMBRANE1 MADE-OF PAPER
CONTACT-ADHESIVE1 GLUES METAL and CHASSIS-ASSEMBLY1 MADE-OF METAL
Construct the plausible version space rule learned from them.
 G.Tecuci, Learning Agents Laboratory
Exercise
Compose an example analogous with the following one:
PAPER
MADE-OF
GLUES
MEMBRANE
1
STATE
fluid
CONTACT
-ADHESIVE
1
GLUES
CHASSIS
-ASSEMBLY
1
MADE-OF
METAL
explains
IF the task is
ATTACH
OBJECT
MEMBRANE
1TO CHASSIS
-ASSEMBLY
1
THEN decompose this task into the subtasks
APPLY
OBJECT
CONTACT
-ADHESIVE
1 ON MEMBRANE
1
PRESS
OBJECT
MEMBRANE
1 ON CHASSIS
-ASSEMBLY
1
 G.Tecuci, Learning Agents Laboratory
Exercise
Rule
IF the task to accomplish is
ATTACH OBJECT ?X TO ?Y
Plausible Upper Bound IF
?X
IS
MADE-OF
?Y
IS
MADE-OF
?Z
IS
GLUES
GLUES
?M
IS
?N
IS
SOMETHING
?M
SOMETHING
?N
ADHESIVE
?M
?N
MATERIAL
MATERIAL
Plausible Lower Bound IF
?X
IS
MADE-OF
?Y
IS
MADE-OF
?Z
IS
GLUES
GLUES
?M
IS
?N
IS
MEMBRANE1
?M
CHASSIS-ASSEMBLY1
?N
CONTACT-ADHESIVE1
?M
?N
PAPER
METAL
THEN accomplish the tasks
APPLY OBJECT ?Z ON ?X
PRESS OBJECT ?X ON ?Y
 G.Tecuci, Learning Agents Laboratory
Find a minimal
generalization of the rule
that covers the positive
example.
Positive Example
IF the task to accomplish is
ATTACH OBJECT BOLT1 TO MECHANICAL-CHASSIS1
THEN accomplish the tasks
APPLY OBJECT MOWICOLL1 ON MECHANICAL-CHASSIS1
PRESS OBJECT BOLT1 ON MECHANICAL-CHASSIS1
Exercise
IF the task to accomplish is
ATTACH OBJECT ?X TO ?Y
Rule
Plausible Upper Bound IF
?X
IS
MADE-OF
?Y
IS
MADE-OF
?Z
IS
GLUES
GLUES
?M
IS
?N
IS
SOMETHING
?M
SOMETHING
?N
ADHESIVE
?M
?N
MATERIAL
MATERIAL
Plausible Lower Bound IF
?X
IS
MADE-OF
?Y
IS
MADE-OF
?Z
IS
GLUES
GLUES
?M
IS
?N
IS
MEMBRANE1
?M
LOUDSPEAKER-COMPONENT
?N
LOUDSPEAKER-COMPONENT
?M
?N
MATERIAL
METAL
THEN accomplish the tasks
APPLY OBJECT ?Z ON ?X
PRESS 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)
 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.
Negative Example
IF the task to accomplish is
ATTACH OBJECT SCREENING-CAP1 TO LOUDSPEAKER1
THEN accomplish the tasks
APPLY OBJECT SCOTCH-TAPE1 ON SCREENING-CAP1
PRESS OBJECT SCREENING-CAP1 ON LOUDSPEAKER1
Exercise
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?
 G.Tecuci, Learning Agents Laboratory
Exercises
The following exercises, marked S1 to S7, are based on the following
semantic network from the loudspeaker manufacturing domain:
SOMETHING
AIR-MOVER
CLEANER
LOUDSPEAKER
-COMPONENT
SOFT-CLEANER HARD-CLEANER
DAMAGES
EMERY-PAPER
REMOVES WASTE-MATERIAL
AIR-JET-DEVICE SOLVENT
ENTREFER MEMBRANE
REMOVES
REMOVES
MAY-HAVE MAY-HAVE
AIR-PRESSAIR-SUCKERACETONE ALCOHOL DUST SURPLUS
-ADHESIVESURPLUS
-PAINT
Remark: Consider that each most specific concept, such as DUST or
AIR-PRESS, has an instance, such as DUST1 or AIR-PRESS1.
 G.Tecuci, Learning Agents Laboratory
Exercise
S1.
Consider the following two expressions:
E1:
?X
?Y
?Z
E2:
?X
?Y
?Z
IS
REMOVES
IS
MADE-OF
IS
SOFT-CLEANER
?Z
MEMBRANE
?T
WASTE-MATERIAL
IS
REMOVES
NOT-DAMAGES
IS
MADE-OF
IS
AIR-SUCKER
?Z
PAPER
MEMBRANE
PAPER
DUST
Use the generalization rules to show that E1 is more general than E2.
 G.Tecuci, Learning Agents Laboratory
Exercise
S2.
Determine the generalization of the following two expressions:
E1:
?x
?y
IS
?z
E2:
?x
?y
?z
 G.Tecuci, Learning Agents Laboratory
IS
MAY-HAVE
IS
REMOVES
entrefer
?y
dust
air-sucker
?y
IS
MAY-HAVE
IS
IS
TYPE
REMOVES
membrane
?y
surplus-adhesive
alcohol
fluid
?y
Exercise
S3.
Consider the following description:
?z
IS
REMOVES
cleaner
surplus-paint
Determine all the possible values of ?z.
 G.Tecuci, Learning Agents Laboratory
Exercise
S4.
Consider the following action description:
CLEAN
OBJECT
OF
WITH
?x
?y
?z
Condition
?x
?y
?z
IS
MAY-HAVE
IS
IS
REMOVES
entrefer
?y
something
cleaner
?y
Find all the possible values for the variables ?x, ?y and ?z.
Indicate some of the corresponding actions.
 G.Tecuci, Learning Agents Laboratory
S5.
Consider the following rule:
IF the task to perform is
CLEAN OBJECT ?x OF ?y
Condition
?x IS
MAY-HAVE
?y IS
?z IS
REMOVES
something
?y
something
cleaner
?y
THEN perform the task
CLEAN 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
Exercise
S6. Consider the following rule:
IF the task to perform is
CLEAN OBJECT ?x OF ?y
Condition
?x IS
MAY-HAVE
?y IS
?z IS
REMOVES
something
?y
something
cleaner
?y
THEN perform the task
CLEAN 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.
 G.Tecuci, Learning Agents Laboratory
Exercise
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 is
CLEAN 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 task
CLEAN OBJECT ?x OF ?y WITH ?z
IF the task to perform is
CLEAN OBJECT membrane1 OF surplus-adhesive1
THEN perform the task
CLEAN OBJECT membrane OF surplus-adhesive1 WITH alcohol1
 G.Tecuci, Learning Agents Laboratory
Exercise
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
Exercise
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
Exercise
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
Exercise
Insert the additional knowledge that platypus lays eggs
into the following object ontology:
birth-mode
mammal
subclass-of
subclass-of
cow
Explain the result.
 G.Tecuci, Learning Agents Laboratory
platypus
live
Exercise
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).
 G.Tecuci, Learning Agents Laboratory
Exercise
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-MECHCOMPANY1, BLUE-MECH-COMPANY2, BLUEARMOR-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).
 G.Tecuci, Learning Agents Laboratory
Exercise
Consider the background knowledge represented by the following generalization
hierarchies:
any-shape
any-color
warm-color
red
orange
polygone
cold-color
yellow
blue
green
triangle
black
rectangle
round
circle
ellipse
square
Consider also the following concept:
E: ?u IS
object
COLOR yellow
SHAPE circle
RADIUS 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
Exercise
Formalize the following tasks:
I need to
Identify and test a strategic COG candidate for
Okinawa_1945 which is a major theater of war scenario
Which is an opposing force in the Okinawa_1945 scenario?
US_1945
Therefore I need to
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
Therefore I need to
Identify and test a strategic COG candidate for US_1945 which is a single-member force
 G.Tecuci, Learning Agents Laboratory
Exercise
Consider the following problem solving
episode and its explanation, in the
context of the background knowledge
the following four slides:
War_materiel_and_transports_of_US_1943
is_a_major_generator_of
Industrial_capacity_of_US_1943
US_1943
has_as_industrial_factor
explains
IF the task to accomplish is
Identify the strategic COG candidates with respect
to the industrial civilization of a force
The force is US_1943
THEN
A strategic COG relevant factor is strategic COG
candidate for a force
The force is US_1943
The strategic COG relevant factor is
Industrial_capacity_of_US_1943
 G.Tecuci, Learning Agents Laboratory
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.
Feature definitions
has_as_industrial_factor
D: Force
R: Industrial_factor
is_a_major_generator_of
D: Economic_factor
R: Product
The force is
D: task
R: Force
The strategic COG relevant factor is
D: task
R: Force
 G.Tecuci, Learning Agents Laboratory
Economic factors
Economic_factor
Other_
economic_
factor
Commerce_
authority
Raw_material
Strategic_
Raw_
Material
Information_
Network_or_system
Transportation_
Factor
Industrial_
factor
Industrial_
authority
Industrial_
Center
Industrial_
Capacity
is_critical_to_
Oil_chromium_
copper_and_bauxite_ the_production_of
of_Germany_1943
War_materiel_of
has_as_strategic_
_Germany_1943
raw_material
is_obtained_from
Farm_implement_industry
Germany_1943
 G.Tecuci, Learning Agents Laboratory
Balkans
Transportation_
Center
Transportation_
Network_or_system
industrial_capacity_
of_US_1943
is_a_major_generator_of
has_as_industrial_factor
war_materiel_and_
transports_of_
US_1943_
US_1943
Farm_implement_industry_of_Italy_1943
Generalization hierarchy of forces
<object>
Force
Group
Opposing_force
Multi_state_force
Single_state_force
component_state
Anglo_allies_1943
component_state
component_state
European_axis_1943
component_state Italy_1943
 G.Tecuci, Learning Agents Laboratory
Multi_group_force
US_1943
Britain_1943
Germany_1943
Single_group_force
Fragment of the generalization hierarchy
<object>
Resource_or_
infrastructure_element
Resource
Product
Strategically_essential_resource_
or_infrastructure_element
Raw_material
Non-strategically_essential
goods_or_services
Strategically_essential_
goods_or_materiel
Strategic_raw_material
Farm-implements
Strategically_essential_
infrastructure_element
War_materiel_and_transports
War_materiel_and_fuel
Main_airport
Sole_airport
 G.Tecuci, Learning Agents Laboratory
Main_seaport
Sole_seaport
War_materiel_and_fuel_
of_Germany_1943
Farm-implements
of_Italy_1943
War_materiel_and_
transports_of_US_1943
Exercise
IF
Identify the strategic COG candidates with respect to
the industrial civilization of a force
The force is ?O1
explanation
?O1 has_as_industrial_factor ?O2
?O2 is_a_major_generator_of ?O3
Plausible Upper Bound Condition
?O1 IS Force
has_as_industrial_factor ?O2
?O2
?O3
IS Industrial_factor
is_a_major_generator_of
IS
?O3
Product
Minimally generalize the rule to
cover the following positive
example (considering the
background knowledge from the
previous four slides):
Positive example that satisfies the upper bound
IF the task to accomplish is
Identify the strategic COG candidates with
respect to the industrial civilization of a force
The force is Germany_1943
THEN accomplish the task
Plausible Lower Bound Condition
?O1
IS US_1943
has_as_industrial_factor
?O2
?O2
IS Industrial_capacity_of_US_1943
is_a_major_generator_of ?O3
?O3
IS
A strategic COG relevant factor is strategic COG
candidate for a force
The force is Germany_1943
The strategic COG relevant factor is
Industrial_capacity_of_Germany_1943
War_materiel_and_transports_of_US_1943
THEN
A strategic COG relevant factor is strategic COG
candidate for a force
The force is ?O1
The strategic COG relevant factor is ?O2
 G.Tecuci, Learning Agents Laboratory
explanation
Germany_1943 has_as_industrial_factor
Industrial_capacity_of_Germany_1943
Industrial_capacity_of_Germany_1943
is_a_major_generator_of
War_materiel_and_fuel_of_Germany_1943
Exercise
IF
Identify the strategic COG candidates with respect to
the industrial civilization of a force
The force is ?O1
explanation
?O1 has_as_industrial_factor ?O2
?O2 is_a_major_generator_of ?O3
Plausible Upper Bound Condition
?O1 IS Force
has_as_industrial_factor ?O2
?O2
?O3
IS Industrial_factor
is_a_major_generator_of
IS
?O3
Product
Plausible Upper Bound Condition
?O1 IS Single_state_force
has_as_industrial_factor ?O2
?O2
?O3
IS Industrial_capacity
is_a_major_generator_of
IS
?O3
Strategically_essential_goods_or_materials
THEN
A strategic COG relevant factor is strategic COG
candidate for a force
The force is ?O1
The strategic COG relevant factor is ?O2
 G.Tecuci, Learning Agents Laboratory
Minimally specialize the rule to
no longer cover the following
negative example (considering
the background knowledge from
the previous slides):
Negative example that satisfies the upper bound
IF the task to accomplish is
Identify the strategic COG candidates with respect
to the industrial civilization of a force
The force is Italy_1943
THEN accomplish the task
A strategic COG relevant factor is strategic COG
candidate for a force
The force is Italy_1943
The strategic COG relevant factor is
Farm_implement_industry_of_Italy_1943
explanation
Italy_1943 has_as_industrial_factor
Farm_implement_industry_of_Italy_1943
Farm_implement_industry_of_Italy_1943
is_a_major_generator_of
Farm_implements_of_Italy_1943
Repertory 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
Exercise
Consider the following two concepts:
C 1:
?X
IS
HEAD
COST
SCREW
HEXAGONAL
5
Indicate different generalization of them.
 G.Tecuci, Learning Agents Laboratory
C 2:
?X
IS
COST
NUT
6
Exercise
Consider the following two concepts and ontology. Indicate four specializations
of G1 and G2 (including a maximally general specialization).
G1: ?X
?M
?Z
IS
LOUDSPEAKER
-COMPONENT G2: ?X
MADE-OFM ?
?M
IS
MATERIAL
?Z
IS
ADHESIVE
GLUES M?
IS
LOUDSPEAKER
-COMPONENT
MADE-OFM ?
IS
MATERIAL
IS
INFLAMMABLE
-OBJECT
GLUES M?
LOUDSPEAKER
-COMPONENT
IS
MEMBRANE
IS
IS
CHASSIS
-ASSEMBLY
BOLT
ADHESIVE TOXIC-SUBSTANCE
INFLAMMABLE
-OBJECT
IS
SCOTCH
-TAPE
IS
IS IS IS
IS
IS
IS
-ADHESIVE
SUPER-GLUEMOWICOLL CONTACT
MATERIAL
IS
CAOUTCHOUC
 G.Tecuci, Learning Agents Laboratory
IS
PAPER
IS
METAL
Exercise
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.
 G.Tecuci, Learning Agents Laboratory
END
 G.Tecuci, Learning Agents Laboratory
Cooperative problem solving and learning
Problem solving with PVS rules
Integrated problem solving and learning
Demonstration
 G.Tecuci, Learning Agents Laboratory
Generalization by analogy
INTELLIGENCE-COLLECTION-MILTARY-TASK
RED-CSOP1
INSTANCE-OF
TASK
SCREEN1
SOVEREIGN-ALLEGIANCE-OF-ORG
RED--SIDE
explain
generalization
IF the task to accomplish is:
Assess security wrt countering enemy reconnaissance
for-coa
COA411
THEN accomplish the task:
Assess security when enemy recon is present
for-coa
COA411
for-unit
RED-CSOP1
for-recon-action SCREEN1
Knowledge-base constraints on the generalization:
Any value of ?O1 should be an instance of:
RANGE(FOR-COA) = COA-SPECIFICATION-MICROTHEORY
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
 G.Tecuci, Learning Agents Laboratory
A positive example covered by the upper bound
Rule: R2
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:
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
COA421
for-unit
RED-CSOP2
for-recon-action SCREEN2
Main
Condition
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
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
THEN accomplish the task:
Assess-security-when-enemy-recon-is-present
for-coa
?O1
for-unit
?O2
for-recon-action ?O3
 G.Tecuci, Learning Agents Laboratory
less general than
?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE
?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
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--SIDE
A negative example covered by the upper bound
Rule: R$ASWCER-001
IF the task to accomplish is:
Assess-security-wrt-countering-enemy-reconnaissance
for-coa ?O1
Question: Is an enemy reconnaissance unit present?
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
Answer: Yes, the enemy unit ?O2 is performing the action ?O3
which is a reconnaissance action.
Explanation:
Main Condition
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
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
THEN accomplish the task:
Assess-security-when-enemy-recon-is-present
for-coa
?O1
for-unit
?O2
for-recon-action
?O3
 G.Tecuci, Learning Agents Laboratory
less general than
•?O2 SOVEREIGN-ALLEGIANCE-OF-ORG ?O4 IS RED--SIDE
•?O2 TASK ?O3 IS INTELLIGENCE-COLLECTION--MIL-TASK
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
ALLEGIANCE-OF-UNIT
SUBCLASS-OF
BLUE-SIDE
_
specialization
RED-SIDE
INTELLIGENCE-COLLECTION-MILTARY-TASK
SUBCLASS-OF
SCREEN-MILITARY-TASK
INSTANCE-OF
SCREEN1
 G.Tecuci, Learning Agents Laboratory
INSTANCE-OF
SCREEN2
COA-SPECIFICATION-MICROTHEORY
INSTANCE-OF
COA411
INSTANCE-OF
COA421