Transcript Document

CS 785 Fall 2004
Gheorghe Tecuci
[email protected]
http://lac.gmu.edu/
Learning Agents Center
and Computer Science Department
George Mason University
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
Semantic networks
The underlying idea of semantic networks is to
represent knowledge in the form of a graph in which the
nodes represent objects, situations, or events, and the
arcs represent the relationships between them.
person
Roman
isa
isa
man
P ompeian
instance-of
instance-of
72
 2004, G.Tecuci, Learning Agents Center
height
Marcus
t ryassassinat e
ruler
instance-of
Caesar
Semantic networks with binary relations
yellow
What does this
semantic network
express?
color
mass
Sun
t emperat ure
MSun
T Sun
at t ract s
greater-t han
greater-t han
revolvesarround
3500
value MEart h
mass
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T Eart h
Earth
t emperat ure
Representing non-binary predicates
What does this
semantic network
express?
bird
isa
nest
robin
instance-of
Clyde
instance-of
owns
nest-1
How can one encode the additional information that Clyde
owned nest-1 from Spring 90 to Fall 90?
A binary relation cannot encode this additional information.
We need to represent ownership as a node rather than a relation.
 2004, G.Tecuci, Learning Agents Center
Representing non-binary predicates
ownership(owner, ownee, start-time, end-time)
bird
isa
robin
instance-of
Clyde
ownership
nest
instance-of
nest -1
owner
instance-of
own-1
ownee
start -t ime
end-t ime
Spring-90
Fall-90
A semantic network representing "ownership" as a node.
 2004, G.Tecuci, Learning Agents Center
Overview
Exercise
Represent the following sentences into a semantic network:
Birds are animals.
Birds have feathers, fly and lay eggs.
Albatros is a bird.
Donald is a bird.
Tracy is an albatros.
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
Ontology representation
1. What is an ontology
2. Sample application domain
3. Concepts, instances, and generalization
4. Object features
5. Definition of instances and concepts
 2004, G.Tecuci, Learning Agents Center
1. What is an ontology
Every knowledge-based agent has a conceptualization or
a model (i.e. an abstract, simplified view) of its world which
consists of representations of the objects, concepts, and
other entities that are assumed to exist, and the
relationships that hold among them.
An ontology is a specification of the terms that are used to
represent the agent’s world.
In an ontology, definitions associate the names of entities in
the agent’s world (e.g., classes, individual objects, relations,
tasks) with human-readable text describing what the names
mean, and formal axioms that constrain the interpretation
and use of these terms.
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Why is the definition of an agent’s ontology important?
Enables an agent to communicate with other agents, because they
share a common vocabulary (terms) which they both understand.
Enables knowledge sharing and reuse among agents.
Ontological commitment:
Agreement among several agents to use a shared
vocabulary in a coherent and consistent manner.
 2004, G.Tecuci, Learning Agents Center
Object ontology
We define an object ontology as a hierarchical
description of the objects from the agent’s domain,
specifying their properties and relationships.
It includes both descriptions of types of objects (called
concepts) and descriptions of specific objects (called
instances).
 2004, G.Tecuci, Learning Agents Center
The generality of the object ontology
An object ontology is characteristic to an entire
application domain, such as military or medicine.
In the military domain the object ontology will include
descriptions of military units and of military equipment.
These descriptions are most likely needed in almost any
specific military application.
Because building the object ontology is a very complex
task, it makes sense to reuse these descriptions when
developing a knowledge base for another military
application, rather than starting from scratch.
 2004, G.Tecuci, Learning Agents Center
2. Sample application domain
Center of Gravity Analysis
Identify COG candidates
Identify potential primary
sources of moral or physical
strength, power and
resistance from:
Government
Military
Test each identified COG
candidate to determine whether
it has all the necessary critical
capabilities:
Which are the critical
capabilities?
People
Are the critical requirements of
these capabilities satisfied?
Economy
If not, eliminate the candidate.
Alliances
If yes, do these capabilities
have any vulnerability?
Etc.
 2004, G.Tecuci, Learning Agents Center
Test COG candidates
3. Concepts, instances, and generalization
A concept is a representation of a set of instances.
state_government
instance_of
government_of_US_1943
instance_of
Represents the set of all entities
that are governments of states.
This set includes
“government_of_US_1943” and
government_of_Britain_1943”
government_of_Britain_1943
state_government
government_of_US_1943
government_of_Britain_1943
Provide another example of a concept.
 2004, G.Tecuci, Learning Agents Center
Characterization of instances
An instance is a representation of a particular entity in
the application domain.
state_government
instance_of
government_of_US_1943
instance_of
government_of_Britain_1943
Represents the entity called “government_of_US_1943”
“instance_of” is the relationship between an instance and the concept to
which it belongs.
Provide another example of an instance and of a concept.
 2004, G.Tecuci, Learning Agents Center
Intuitive definition of generalization
Intuitively, a concept P is said to be more general than (or a
generalization of) another concept Q if and only if the set of
instances represented by P includes the set of instances
represented by Q.
Example:
state_government
democratic_government
representative_
democracy
parliamentary_
democracy
totalitarian_
government
state_government
“subconcept_of” is the relationship between
a concept and a more general concept.
subconcept_of
democratic_government
 2004, G.Tecuci, Learning Agents Center
Intuitive definition of generalization (cont.)
What are the possible relationships between two concepts
A and B, from a generalization point of view?
- A is more general than B
- B is more general than A
- There is no generalization relationship between A and B
Provide examples of concepts A and B in each of these
three situations.
 2004, G.Tecuci, Learning Agents Center
Intuitive definition of generalization (cont.)
How could one prove that A is more general than B?
Show that all the instances of B are also instances of A
B
Is this always a practical procedure?
A
No if A and B have an infinite number of instances.
How can one prove that A is not more general than B?
Show that B contains an instance which is not an
instance of A.
.
B
A
Is this a more practical procedure?
Yes, even for concepts with an infinite number of instances.
 2004, G.Tecuci, Learning Agents Center
A generalization hierarchy
governing_body
ad_hoc_ governing_body
established_ governing_body
other_type_of_
governing_body
state_government
group_governing_body
feudal_god_
king_government
other_state_
government
democratic_
government
monarchy
other_
group_
governing_
body
dictator
deity_figure
representative_ parliamentary_
democracy
democracy
government_
of_Italy_1943
totalitarian_
government
police_
state
government_
of_US_1943
government_
of_Britain_1943
military_
dictatorship
religious_
dictatorship
democratic_
council_
or_board
autocratic_
leader
fascist_
state
communist_
dictatorship
 2004, G.Tecuci, Learning Agents Center
government_
of_USSR_1943
theocratic_
government
religious_
dictatorship
government_
of_Germany_1943
theocratic_
democracy
chief_and_
tribal_council
Partially learned concepts and version spaces
Question: What is the concept represented by the following two positive examples
P = {government_of_US_1943, government_of_Britain_1943}
and the following negative example
N = {GMU_governing_body_2004} ?
governing_body
established_governing_body
.
state_government
.
democratic_government
representative_
democracy
 2004, G.Tecuci, Learning Agents Center
.
parliamentary_
democracy
totalitarian_
government
Partially learned concepts and version spaces
Question: What is the concept represented by the following two positive examples
P = {government_of_US_1943, government_of_Britain_1943}
and the following negative example
N = {GMU_governing_body_2004} ?
.
Upper bound of the VS
state_government
.
Lower bound of the VS
democratic_government
 2004, G.Tecuci, Learning Agents Center
.
Version Space (VS)
upper bound: {state_government}
lower bound: {democratic_government}
Object descriptions
The objects in the application domain may be described
in terms of their properties and their relationships with
each other.
parliamentary_democracy
instance_of
has_as_head_of_government
Winston_Churchill
government_of_Britain_1943
has_as_legislative_body
parliament_of_Britain_1943
government_of_Britain_1943 has_as_legislative_body
parliament_of_Britain_1943
Similarly, a general object or concept can be described as being a
subconcept of an even more general concept and having additional features.
 2004, G.Tecuci, Learning Agents Center
Feature definition
An object feature is itself characterized by several features
which include: documentation, domain and range.
The domain is the concept that represents the set of objects
that could have that feature.
The range is the set of possible values of the feature.
has_as_political_leader
subconcept_of
has_as_head_of_government
documentation
domain
range
 2004, G.Tecuci, Learning Agents Center
"Indicates the head
of a governing body"
governing_body
person
Partially learned feature
Allied_Forces_1943
has_as_strategic_goal
documentation
has_as_strategic_goal
domain
range
 2004, G.Tecuci, Learning Agents Center
Unconditional_surrender_of_European_Axis_1943
"Indicates the strategic goal of an entity”
plausible upper bound:
agent
plausible lower bound:
force
plausible upper bound:
agent_goal
plausible lower bound:
force_goal
Feature hierarchy
has_as_controlling_leader
D: agent
R: person
has_as_religious_leader
D: governing_body
R: person
has_as_god_king
D: governing_body
R: person
has_as_monarch
D: governing_body
R: person
has_as_military_leader
D: governing_body
R: person
has_as_political_leader
D: governing_body
R: person
has_as_head_of_government
D: governing_body
R: person
 2004, G.Tecuci, Learning Agents Center
has_as_commander_in_chief
D: force
R: person
has_as_head_of_state
D: governing_body
R: person
Exercise
Consider the following feature hierarchy:
DOMAIN
1D1
RANGE
1R1
Is there any relationship between:
Feature
1
BD1 and 1D1?
DOMAIN
BD1
Feature B
A2D1 and 1D1?
RANGE
BR1
DOMAIN
AD1
RANGE
AR1
AD1 and BD1?
Feature A
Feature A1
1D1 and 1R1?
DOMAIN
A2D1
RANGE
A2R1
Feature A2
 2004, G.Tecuci, Learning Agents Center
5. Definition of instances and concepts
When designing a knowledge base, one has to first specify
some basic concepts, as well as the features that may
characterize the instances and the concepts from the
application domain.
Once basic concepts and features are specified, one can
define new concepts and instances as logical expressions of
the specified concepts and features.
 2004, G.Tecuci, Learning Agents Center
Basic representation unit
conceptk
ISA
FEATURE1
...
FEATUREn
concepti
value1
valuen
This is a necessary definition of ‘conceptk’.
It defines ‘conceptk’ as being a subconcept of ‘concepti’ and
having additional features.
This means that if ‘concepti’ represents the set Ci of instances,
then ‘conceptk’ represents a subset Ck of Ci.
The elements of Ck have the features ‘FEATURE1’,...,
‘FEATUREn’ with the values ‘value1’,..., ‘valuen’, respectively.
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
Reasoning with an ontology
1. Transitivity of “instance_of” and “subconcept_of”
2. Inheritance
3. Concept expressions
4. Ontology matching
5. Rules as ontology-based representations
 2004, G.Tecuci, Learning Agents Center
1. Transitivity of “instance_of” and “subconcept_of”
established_governing_body
established_governing_body
subconcept_of
state_government
state_government
subconcept_of
subconcept_of
democratic_government
democratic_government
subconcept_of
representative_democracy
instance_of
instance_of
government_of_US_1943
government_of_US_1943
 2004, G.Tecuci, Learning Agents Center
2. Inheritance
Properties associated with concepts in a hierarchy are assumed to be
true of all subconcepts and instances.
democratic_
government
has_as_dominant_
“will of the people”
psychosocial_factor
representative_
democracy
government_
of_US_1943
 2004, G.Tecuci, Learning Agents Center
has_as_political_leader
President_Roosevelt
Exceptions to default inheritance
Properties associated with concepts in a hierarchy are
assumed to be true of all subconcepts and instances.
How can we deal with exceptions (i.e. sub-concepts or
instances that do not have the inherited property)?
Explicitly override the inherited property, as illustrated in the
following slide.
 2004, G.Tecuci, Learning Agents Center
Exceptions to default inheritance: illustration
democratic_
government
has_as_dominant_
“will of the people”
psychosocial_factor
representative_
democracy
government_
of_US_1943
has_as_political_leader
President_Roosevelt
parliamentary_ has_as_dominant_psychosocial_factor
{“will of the people” “will of the parliament”}
democracy
has_as_dominant_psychosocial_factor
“will of the people”
government_
of_Britain_1943
has_as_political_leader
 2004, G.Tecuci, Learning Agents Center
Winston_Churchill
Multiple inheritance
An object (instance or concept) may inherit properties from
several super-concepts.
How can we deal with the inheritance of contradictory
properties?
Explicitly state the property of the object, instead of inheriting
it:
south pole
habitat
cartoon character
penguin
instance-of
instance-of
Opus
 2004, G.Tecuci, Learning Agents Center
habitat
funny papers
3. Concept expressions
One can define more complex concepts as logical expressions
involving the basic concepts from the object ontology.
In the following expression, for instance, ?O1 represents a
force that has as industrial factor an industrial capacity that
generates essential war materiel from the strategic perspective
of a multi-member force that includes ?O1.
?O1
?O2
?O3
is
force
has_as_industrial_factor
?O2
is
industrial_capacity
generates_essential_war_materiel_from_
the_strategic_perspective_of
is
multi_member_force
has_as_member
?O1
 2004, G.Tecuci, Learning Agents Center
?O3
4. Ontology matching
Ontology matching allows one to answer complex
questions about the knowledge represented in the
ontology, as illustrated in the following:
Question:
Is there any force ?O1 that has as industrial factor an
industrial capacity that generates essential war materiel
from the strategic perspective of a multi-member force
that includes ?O1?
Answer:
Yes, US_1943 is a force that has as industrial factor
industrial_capacity_of_US_1943 that generates essential
war materiel from the strategic perspective of the
Allied_Forces_1943 which is a multi-member force that
includes US_1943.
 2004, G.Tecuci, Learning Agents Center
Ontology matching: example
Question
Object ontology
Answer
?O1  US_1943
force
?O2  industrial_capacity_
of_US_1943
force
?O3  Allied_forces_1943
instance-of
subconcept-of
single_member_force
subconcept-of
single_state_force
?O1
industrial_capacity
instance-of
has_as_industrial_factor
instance-of
multi_state_force
has_as_member
instance-of
?O2
generates_essential_
war_materiel_from_ the_
strategic_perspective_of
?O3
Is there any force ?O1 that has as
industrial factor an industrial capacity
that generates essential war materiel
from the strategic perspective of a
multi-member force that includes ?O1?
 2004, G.Tecuci, Learning Agents Center
US_1943
industrial_capacity
has_as_industrial_factor
instance-of
Industrial_capacity_
multi_state_force
of_US_1943
subconcept-of
multi_state_
alliance
subconcept-of
generates_essential_
equal_partner_
war_materiel_from_ the_
multi_state_
strategic_perspective_of
alliance
has_as_member
instance-of
Allied_forces_1943
5. Rules as ontology-based representations of PSS
IF
Identify and test a strategic COG candidate corresponding
to the economy of a force which is an industrial economy
The industrial economy is ?O1
Condition
?O1 is industrial_economy
?O2
is industrial_capacity
generates_essential_war_materiel_from_
the_strategic_perspective_of ?O3
?O3 is multi_state_force
has_as_member ?O4
?O4 is force
has_as_economy ?O1
has_as_industrial_factor ?O2
THEN
Identify a strategically critical element as a COG candidate
with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
Test a strategically critical element which is a strategic
COG candidate with respect to an industrial economy
The strategically critical element is ?O2
The industrial economy is ?O1
A rule is an ontology-based representation
of a problem solving step (PSS).
 2004, G.Tecuci, Learning Agents Center
Condition:
?O1 is an industrial economy
?O2 is an industrial capacity
which generates essential war
material from the strategic
perspective of ?O3, a multistate force which has as
member ?O4 (a force which
has as economy ?O1 and as
industrial factor ?O2).
This rule will be
applicable only if the
current ontology contains
instances of the concepts
?O1, ?O2, ?O3, and ?O4
represented in the
condition.
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
Ontology maintenance
Maintaining the consistency of the object ontology is a
complex knowledge engineering activity because the
object and feature definitions interact in complex ways.
Example:
Deleting an object concept requires the updating of all
the knowledge base elements that refer to it (e.g. the
rules that contain it in their conditions; the features that
contain it in their ranges or domains; the concepts that
inherit its features).
 2004, G.Tecuci, Learning Agents Center
Potential consequence of editing operations: Illustration
f
domain
A
f
domain
A
A
A
B
B
C
Initial State
f
7
C
f
Modified State
Explain why.
Because C is no longer in the domain of f.
 2004, G.Tecuci, Learning Agents Center
If we delete
the link from
B to A, then C
can no longer
have the
feature f
7
Steps in ontology development
1. Define the basic concepts, and their organization into
a hierarchical structure (the generalization hierarchy).
2. Define the generic features, using the previously
defined concepts to specify their domains and
ranges.
3. Use the defined concepts and feature to define
instances.
4. Extend the ontology with new concepts, features, and
instances.
5. Repeat steps 1,2,3,4 until the ontology is judged to be
complete enough.
 2004, G.Tecuci, Learning Agents Center
Steps in ontology development: illustration
object
resource_or_infrastructure_element
agent_goal
scenario
agent
force_goal
1. Define basic
concepts
strategic_goal
strategic_cog_relevant_factor
force
other_relevant_factor
operational_goal
theater_strategic_goal
psychosocial_factor
political_factor
demographic_factor
military_factor
economic_factor
historic_factor
geographic_factor
international_factor
has_as_force_goal
documentation
2. Use the basic concepts
to define features
has_as_strategic_goal
has_as_operational_goal
3. Use the defined concepts and
features to define instances.
 2004, G.Tecuci, Learning Agents Center
domain
range
"Indicates the strategic goal of an entity”
plausible upper bound:
agent
plausible lower bound:
force
plausible upper bound:
agent_goal
plausible lower bound:
force_goal
force_goal
force
has_as_strategic_goal
Allied_Forces_1943
Unconditional_surrender_of_European_Axis_1943
General features of semantic networks and ontologies
Representational adequacy:
High for binary relationships.
Inferential adequacy:
Good for certain types of inferential procedures.
Inferential efficiency: very high
The structure used for representing knowledge
is also a guide for the retrieval of the knowledge.
Acquisitional efficiency: very low
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
The object hierarchy
Overall organization
Scenario
Forces and goals
Economic factors
Geographic factors
Military factors
Political factors
Other objects
 2004, G.Tecuci, Learning Agents Center
Overall organization of the COG object ontology
object
resource_or_infrastructure_element
agent_goal
scenario
force_goal
strategic_goal
agent
strategic_cog_relevant_factor
force
other_relevant_factor
operational_goal
theater_strategic_goal
psychosocial_factor
political_factor
demographic_factor
military_factor
economic_factor
historic_factor
geographic_factor
 2004, G.Tecuci, Learning Agents Center
international_factor
subconcept_of
Scenario
object
scenario
agent
war_scenario
force
Sicily_1943
brief_description
description
opposing_force
“The Allied decision to invade Sicily following the successful operation in North Africa was a
critical element of World War II [WWII]. The commitment of such a large force to continue
operations in the Mediterranean theater meant that the cross-channel invasion of Europe would
be delayed.
American military leaders strongly favored the cross-channel invasion at the earliest
possible opportunity. This meant giving this invasion force first priority for troops, shipping
and equipment. The British favored an indirect approach that would see a major effort
continue in the Mediterranean. The Allies settled on the Mediterranean approach at the
Casablanca conference in January 1943 and began planning for Operation Husky, the invasion
of Sicily.
Situated ninety miles off the north coast of Africa and two and one-half miles from the toe
of the Italian peninsula, Sicily was both a natural bridge between Africa and Europe and a
barrier dividing the Mediterranean Sea. It was an unsinkable air and naval fortress from which
Axis forces interdicted Allied sea lines of communications through the Mediterranean.
…”
has_as_opposing_force
has_as_opposing_force
 2004, G.Tecuci, Learning Agents Center
“WWII Allied invasion of Sicily in 1943”
Allied_Forces_1943
European_Axis_1943
Forces
force
military_force
group
opposing_force
multi_member_force
multi_group_force
supporting_force
single_member_force
legal_group
illegal_group
single_state_force
multi_state_force
single_group_force
multi_state_alliance multi_state_coalition
dominant_partner_
equal_partners_
multi_state_alliance multi_state_alliance
has_as_member
Allied_Forces_1943
has_as_member
has_as_member
has_as_member
European_Axis_1943
has_as_member
 2004, G.Tecuci, Learning Agents Center
Germany_1943
Italy_1943
US_1943
Britain_1943
USSR_1943
Forces (continued)
group
single_group_force
legal_group
illegal_group
…
legal_business_group
clan_or_tribe
other_legal_group
military_group_or_clan
military_group_or_unit
military_clan
para_military_group
 2004, G.Tecuci, Learning Agents Center
Forces (continued)
illegal_group
other_illegal_group
profit_focused_illegal_group
crime_gang
crime_family
drug_cartel
vice_ring
purpose_focused_illegal_groups
hate_group
terrorist_cell
extremist_group
 2004, G.Tecuci, Learning Agents Center
separatists
religious_cult
personality_cult
Force goals
force_goal
strategic_goal
Allied_Forces_1943
has_as_strategic_goal Unconditional_surrender_ description “The strategic goals of the US-British alliance in
of_European_Axis_1943
1943 were to defeat Germany first while
containing Japan, to keep Russia in the war on
the side of the Allies, and the eventual
unconditional surrender of all Axis countries. To
theater_strategic_goal
accomplish these goals, US leaders favored an
has_as_theater_strategic_goal
early, direct attack into northern Europe. …”
Taking_Italy_out_of_war_
description
against_Allies_and_keeping_
“…“
USSR_in_war_alongside_Allies
operational_goal
has_as_operational_goal
 2004, G.Tecuci, Learning Agents Center
Capturing_of_the_
island_of_Sicily
description
“The operational goals of the Allies were to
secure sea lines of communications in the
Mediterranean Sea, divert German combat
power from the Russian front, and knock
Italy out of the war. …”
Economic factors
economic_factor
other_
economic_
factor
commerce_
authority
information_
network_or_system
raw_material
transportation_
factor
industrial_
factor
strategic_
raw_
material
industrial_
authority
oil_chromium_
copper_and_bauxite_
of_Germany_1943
transportation_
center
war_materiel_of
_Germany_1943
produces_war_materiel_for
is_obtained_from
 2004, G.Tecuci, Learning Agents Center
transportation_
network_or_system
industrial_capacity_
of_US_1943
has_as_strategic_
raw_material
Germany_1943
…
industrial_
capacity
industrial_
center
is_critical_to_
the_production_of
type_of_
economy
Balkans
has_as_industrial_factor
US_1943
US_1943_
Economic factors (continued)
type_of_economy
industrial_economy
other_type_of_economy
informational_economy
economy_of_US_1943
has_as_economy
US_1943
 2004, G.Tecuci, Learning Agents Center
pre_industrial_economy
Geographic factors
geographic_factor
place
geographic_
center_of_power
“The US has extremely long
and external lines of
communication. Some US
forces deployed directly
from the US for the invasion
of North Africa and much of
the shipping and naval forces
for Sicily will come directly
from the US for operation
Husky.”
capital
US_1943
has_as_capital
Washington_DC
description
has_as_other_geographic_factor
 2004, G.Tecuci, Learning Agents Center
other_geographic_factors_of_US_1943
Military factors
military_factor
military_requirement
composition_of_forces
other_military_factor
military_capability
…
critically_important_
military_requirement
single_member_force
critically_important_
military_capability
multi_member_force
nuclear_weapons
single_state_force
single_group_force
multi_state_alliance
dominant_partner_
multi_state_alliance
European_
Axis_1943
multi_state_coalition
dominant_partner_
multi_state_coalition
equal_partners_
multi_state_alliance
Allied_Forces_1943
 2004, G.Tecuci, Learning Agents Center
multi_state_force
multi_group_force
multi_group_alliance
dominant_partner_
multi_group_alliance
equal_partners_
multi_state_coalition
multi_group_coalition
dominant_partner_
multi_group_coalition
equal_partners_
multi_group_alliance
equal_partners_
multi_group_coalition
Military factors (cont.)
other_military_factor
essential_military_contribution
geographical_entre
manpower
significance_of_
military_contribution
critical_terrain
secondary_military_contribution
essential_war_materiel
minor_military_contribution
most_important_
military_contribution
important_indirect_
military_contribution
military_contribution_of_USSR_1943
shared_primary_
military_contribution
military_contribution_of_US_1943
has_as_military_contribution
has_as_military_contribution
USSR_1943
 2004, G.Tecuci, Learning Agents Center
US_1943
Political factors
political_factor
governing_body
 2004, G.Tecuci, Learning Agents Center
controlling_element
other_political_factor
Political factors: Governing body
governing_body
ad_hoc_ governing_body
established_ governing_body
other_type_of_
governing_body
state_government
group_governing_body
feudal_god_
king_government
other_state_
government
democratic_
government
monarchy
other_
group_
governing_
body
dictator
deity_figure
representative_ parliamentary_
democracy
democracy
government_
of_Italy_1943
totalitarian_
government
police_
state
government_
of_US_1943
government_
of_Britain_1943
military_
dictatorship
religious_
dictatorship
democratic_
council_
or_board
autocratic_
leader
fascist_
state
communist_
dictatorship
 2004, G.Tecuci, Learning Agents Center
government_
of_USSRy_1943
theocratic_
government
religious_
dictatorship
government_
of_Germany_1943
theocratic_
democracy
chief_and_
tribal_council
Political factors: Controlling elements
controlling_element
controlling_leader
other_controlling_element
people
god_king
monarch
other_
controlling_
leader
controlling_organization
ceo
religious_leader
military_leader
Secret_police
para_military_
group_leader
controlling_group
political_leader
head_of_state
head_of_government
religious_body
military_staff
dominant_clan_leader
 2004, G.Tecuci, Learning Agents Center
Ruling_political_party
clan_leader
political_cabinet_
or_staff
Conventional_
law_enforcement_
organization
Governing and controlling elements
democratic_
government
representative_
democracy
government_
of_US_1943
has_as_head_
of_government
has_as_legislative_body
has_critical_role_in_
President_Roosevelt setting_objectives_for
Congress_of_US
has_critical_role_in_
decision_making_for
has_as_ruling_political_party Democratic_Party
has_as_dominant_psychosocial_factor
parliamentary_
democracy
will_of_the_people_
of_US_1943
has_as_governing_body
has_as_dominant_psychosocial_factor will_of_the_parliament_
of_Britain_1943
has_as_head_
of_government
Winston_Churchill
government_ has_as_political_cabinet_or_staff
War_cabinet
of_Britain_1943
has_as_legislative_body
parliament_of_Britain_1943
 2004, G.Tecuci, Learning Agents Center
US_1943
Governing and controlling elements (cont.)
head_of_government
fascist_
state
has_as_head_
of_government
Adolph_Hitler
has_as_ruling_political_party
government_of
_Germany_1943
has_as_secret_police
Nazi Party
Gestapo
has_as_military_staff
OKW
 2004, G.Tecuci, Learning Agents Center
Other objects
object
resource_or_
infrastructure_element
resource
raw_material
strategically_essential_resource_
or_infrastructure_element
product
Strategic_raw_material
Oil_chromium_copper_
bauxite_of_Germany_1943
strategically_essential_
infrastructure_element
main_airport
sole_airport
drugs
strategically_essential_
goods_or_materiel
main_seaport
war_materiel
fuel
sole_seaport
essential_campaign_war_materiel
 2004, G.Tecuci, Learning Agents Center
landing_craft_and_fighter_aircraft_of_US_1943
farmimplements
The feature hierarchy
Overall organization
Informal descriptions
Goals
Component of
Economic factors
Controlling factors
Geographic factors
Other relevant factors
 2004, G.Tecuci, Learning Agents Center
Overall organization of the feature hierarchy
feature
D: object
R: object
informal_specification
D: object
R: any string
…
has_as_governing_body
D: force
R: agent
has_as_opposing_force
D: scenario
R: force
has_as_force_goal
D: force
R: force_goal
has_as_controlling_element
D: agent
R: agent
…
has_as_component
D: force
R: force
…
 2004, G.Tecuci, Learning Agents Center
…
has_as_economic_factor
D: force
R: economic_factor
…
Overall organization of the feature hierarchy (cont.)
feature
D: object
R: object
type_of_operations
D: force
R: {“independent operations” “joint
operations” “combined operations”
“combined and joint operations”}
has_as_dominant_psychosocial_factor
D: governing_body
R: will_of_agent
has_critical_role_in_activity_for
D: controlling_element
R: force
…
has_as_primary_force_element
D: force
R: force
 2004, G.Tecuci, Learning Agents Center
has_as_dominant_partner
D: force
R: force
has_as_additional_relevant_factor
D: force
R: strategic_cog_relevant_factor
…
has_as_geographic_center_of_power
D: force
R: place
…
has_as_source_of_strength_power_and_resistance
D: force
R: strategic_cog_relevant_factor
Overall organization of the feature hierarchy (cont.)
feature
D: object
R: object
is_critical_to_the_production_of
D: resource
R: product
is_obtained_from
D: resource
R: object
controls
D: economic_factor
R: resource_ or_infrastructure_element
produces
D: agent
R: product
generates_essential_war_materiel_from_the_strategic_perspective_of
D: industrial_capacity
R: force
 2004, G.Tecuci, Learning Agents Center
Informal descriptions
informal_specification
D: object
R: any string
brief_description
D: object
R: any string
 2004, G.Tecuci, Learning Agents Center
description
D: object
R: any string
documentation
D: object
R: any string
Goals
has_as_force_goal
D: force
R: force_goal
has_as_strategic_goal
D: force
R: force_goal
 2004, G.Tecuci, Learning Agents Center
has_as_theater_strategic_goal
D: force
R: force_goal
has_as_operational_goal
D: force
R: force_goal
Component of
has_as_component
D: force
R: force
component_state
D: force
R: single_state_force
has_as_first_member
D: multi_member_force
R: force
 2004, G.Tecuci, Learning Agents Center
has_as_member
D: multi_member_force
R: force
has_as_second_member
D: multi_member_force
R: force
has_as_subgroup
D: force
R: group
has_as_third_member
D: multi_member_force
R: force
has_as_additional_member
D: multi_member_force
R: force
Economic factors
has_as_economic_factor
D: force
R: economic_factor
has_as_commerce_authority
D: force
R: economic_factor
has_as_strategic_raw_material
D: force
R: raw_material
has_as_economy
D: force
R: type_of_economy
has_as_information_network_or_system
D: force
R: economic_factor
has_as_transportation_factor
D: force
R: economic_factor
…
has_as_industrial_factor
D: force
R: economic_factor
has_as_industrial_authority
D: force
R: economic_factor
 2004, G.Tecuci, Learning Agents Center
Economic factors (cont.)
has_as_transportation_factor
D: force
R: economic_factor
has_as_transportation_network_or_system
D: force
R: economic_factor
 2004, G.Tecuci, Learning Agents Center
has_as_transportation_center
D: force
R: economic_factor
Controlling factors
has_as_controlling_element
D: agent
R: agent
has_as_controlling_leader
D: agent
R: person
has_as_controlling_organization
D: agent
R: agent
…
…
has_as_controlling_group
D: agent
R: agent
…
 2004, G.Tecuci, Learning Agents Center
Controlling factors (cont.)
has_as_controlling_leader
D: agent
R: person
has_as_religious_leader
D: governing_body
R: person
has_as_god_king
D: governing_body
R: person
has_as_monarch
D: governing_body
R: person
has_as_military_leader
D: governing_body
R: person
has_as_political_leader
D: governing_body
R: person
has_as_head_of_government
D: governing_body
R: person
 2004, G.Tecuci, Learning Agents Center
has_as_commander_in_chief
D: force
R: person
has_as_head_of_state
D: governing_body
R: person
Controlling factors (cont.)
has_as_controlling_group
D: agent
R: agent
has_as_military_staff
D: governing_body
R: agent
has_as_political_cabinet_or_staff
D: governing_body
R: agent
has_as_religious_body
D: governing_body
R: agent
 2004, G.Tecuci, Learning Agents Center
Controlling factors (cont.)
has_as_controlling_organization
D: agent
R: agent
has_as_ruling_political_party
D: governing_body
R: agent
has_as_legislative_body
D: agent
R: agent
 2004, G.Tecuci, Learning Agents Center
has_as_secret_police
D: governing_body
R: agent
has_as_religious_organization
D: agent
R: agent
has_as_conventional_law_enforcement_organization
D: governing_body
R: agent
Controlling factors (cont.)
has_critical_role_in_activity_for
D: controlling_element
R: force
has_critical_role_in_decision_making_for
D: controlling_element
R: force
 2004, G.Tecuci, Learning Agents Center
has_critical_role_in_setting_objectives_for
D: controlling_element
R: force
Geographic factors
has_as_geographic_center_of_power
D: force
R: place
has_as_capital
D: force
R: place
 2004, G.Tecuci, Learning Agents Center
has_as_other_geographic_center_of_power
D: force
R: place
Other relevant factors
has_as_additional_relevant_factor
D: force
R: strategic_cog_relevant_factor
has_as_other_relevant_factor
D: Force
R: Other_relevant_factor
has_as_other_demographic_factor
D: force
R: demographic_factor
has_as_other_political_factor
D: force
R: political_factor
has_as_other_economic_factor
D: force
R: economic_factor
has_as_other_military_factor
D: force
R: military_factor
has_as_other_geographic_factor
D: force
R: geographic_factor
has_as_other_historic_factor
D: force
R: historic_factor
has_as_other_psychosocial_factor
D: force
R: psychosocial_factor
has_as_other_international_factor
D: force
R: international_factor
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
KB Management and Ontology Tools
Object
Hierarchy
Browsers
Ontology
Tools
Feature
Hierarchy
Browsers
Object
Editor
Association
Browser
Feature
Editor
Object
Viewer
Script
Editor
Feature
Viewer
Knowledge Base Management
Ontology
Management
Plausible
Version
Spaces Mngt.
ONTOLOGY
 2004, G.Tecuci, Learning Agents Center
Rules
Management
RULES
These are tools
for a knowledge
engineer,
primarily to
develop and
update the
object ontology.
Association browser
 2004, G.Tecuci, Learning Agents Center
Object hierarchy browser and object viewer
 2004, G.Tecuci, Learning Agents Center
Feature hierarchy browser and Feature viewer
 2004, G.Tecuci, Learning Agents Center
Object editor
 2004, G.Tecuci, Learning Agents Center
Overview
Semantic networks
Ontology representation
Reasoning with an ontology
Development and maintenance of an ontology
Sample ontology for COG analysis
Ontology development tools
Exercises and readings
 2004, G.Tecuci, Learning Agents Center
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.
 2004, G.Tecuci, Learning Agents Center
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.
 2004, G.Tecuci, Learning Agents Center
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.
 2004, G.Tecuci, Learning Agents Center
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.
 2004, G.Tecuci, Learning Agents Center
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.
 2004, G.Tecuci, Learning Agents Center
platypus
live
Exercise
Consider the question: “Is there a part of a loudspeaker that is made of metal?”
In the context of the following object ontology.
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.
SOMETHING
ISA
I N F L A M M-O
A BBLJEE C T
...
ISA
ISA
ISA
ADHESIVE
...
ISA
ISA
M A T E R I A L F R A G-O
I LB J E C T
TOXI
-SCU B S T A N C E
ISA
ISA
GLUES
M O W I C OSLTLA T Ef l u i d
PROVIDER
...
ISA
ISA
GLUES
ISA
M A D E - OMFE M B R A N E
ISA
PAPER
I
N
S
T
A
-O
N
C
F
E
COLOR
M E M B R1A N E
black
PART-OF
C O N T A-ACDTH E S I V EI S A
GLUES
GLUES
I N S T A-O
N CFE
M O W I C1O L L
GL U-EIN C
 2004, G.Tecuci, Learning Agents Center
GLUES
PROVIDER
GLUES
PART-OF
L O U D S P E-C
A KOEMRP O N E N T
ISA
ISA
ISA
LOUDSPEAKER
ISA
METAL
MECHANI
-CCHAALS S I S
C H A S S-I S
M E M B R-A N E
ASSEMBLY
ISA
I N S T A-O
N CFE
CONTAINS
MADE-OF
I N S T A-O
N CFE
ISA
MECHANI
-CCHAALS S1I S
ISA
I N S T A-O
N CFE
ISA
C H A S S-I S
M E M B R-A N E
A S S E M1B L Y
MADE-OF
MADE-OF
C A O U T C H OC
UO
C N T A-ACDTH E S1I V E
BOLT
C H A S S-AISSS E M B L Y
PART-OF
I N S T A-O
N CFE
I N S T A-O
N CFE
C H A S S-AISSS E M1B L Y
B O L1T
Recommended reading
Tecuci G., Lecture Notes on Semantic Networks (on the course web site)
Tecuci G., Building Intelligent Agents, Academic Press, 1998, pp. 33-78.
Gruber T., What is an ontology, http://www-ksl.stanford.edu/kst/what-is-anontology.html (accessed September 29, 2003).
Stanescu B., Boicu C., Balan G., Barbulescu M., Boicu M., Tecuci G., "Ontologies
for Learning Agents: Problems, Solutions and Directions," in Proceedings of the
IJCAI-03 Workshop on Workshop on Ontologies and Distributed Systems,
Acapulco, Mexico, August, AAAI Press, Menlo Park, CA, 2003, pp. 75-82.
http://lac.gmu.edu/publications/data/2003/stanescu-gmu.pdf
Boicu M., Tecuci G., Stanescu B., Balan G.C. and Popovici E., Ontologies and the
Knowledge Acquisition Bottleneck, in Proceedings of IJCAI-2001 Workshop on
Ontologies and Information Sharing, Seattle, Washington, August 2001.
http://lac.gmu.edu/publications/data/2001/ontbottleneck.pdf
Boicu M., Tecuci G., Bowman M., Marcu D., Lee S.W. and Wright K., A ProblemOriented Approach to Ontology Creation and Maintenance, in The Sixteenth
National Conference on Artificial Intelligence (AAAI-99) Workshop on "Ontology
Management", July 18-19, Orlando, Florida, AAAI Press, Menlo Park, CA. 1999.
http://lac.gmu.edu/publications/default.htm
 2004, G.Tecuci, Learning Agents Center