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Part II Methods of AI. Chapter 3 Knowledge and Reasoning. Part II: Methods of AI. Chapter 3 – Knowledge Representation and Reasoning. 3.1 Summary of Logic and Reasoning. 3.2 Reasoning: Deduction Systems. 3.3 Rulebased Reasoning. 3.4 Knowledge Representation: General Issues.

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Part ii methods of ai l.jpg

Part IIMethods of AI

Chapter 3

Knowledge and Reasoning


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Part II: Methods of AI

Chapter 3 – Knowledge Representation and Reasoning

3.1 Summary of Logic and Reasoning

3.2 Reasoning: Deduction Systems

3.3 Rulebased Reasoning

3.4 Knowledge Representation: General Issues

3.5 Knowledge Representation:

Semantic Nets

3.6 Knowledge Representation:

Description Logics

3.7 Knowledge Representation:

Analogue Representation


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3.5 Knowledge

Representation

Semantic Networks


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Semantic

Networks


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Simple and

Naive

Semantic

Nets


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Naive Semantic Networks: to get started!

is-A

I.

ROBIN

BIRD

is-A

is-A

II.

CLYDE

ROBIN

BIRD

is-A

is-A

III.

CLYDE

ROBIN

BIRD

has-part

WINGS


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Semantic Nets:

an „unstructured“ Example

PROBLEM-1: Semantics of the SN?

PROBLEM-2: Structure of the SNs ?

Problem-3: Binary relations?



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Semantic Networks 1: “Schönfinkeln”

Binary Relations versus Relations with Many Arguments

Translation a la Schönfinkel (and other logicians)

EXAMPLE: Own(Owner, Ownee, Start, End)

Owner(own-1, ... )

Ownee(own-1, ... )

Start(own-1, ... )

End(own-1, ... )

TAFEL!

Translates into:


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Semantic Networks: An Example for an n-ary Relation

is-A

is-A

CLYDE

ROBIN

BIRD

is-A

ownee

OWN_1

NEST_1

NEST

is-A

start-time

Spring

TIME

is-A

end-time

Fall

is-A

OWNERSHIP

SITUATION


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Token-Type Distinction in SN‘s

is-A

is-A

CLYDE

ROBIN

BIRD

owns

is-A

NEST_1

NEST

Token: Nest_1

Type: Nest

Nest_1 is of type “Nest”


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Semantic Networks: Platon´s World

Generic Concept: An Arch

  • Generic Concept

  • Individual Concept

  • Individual Object:

PARIS

An Arc de Triomphe


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Inferences with Semantic Nets

CAN-FLY

BIRD

property

is-A

color

CANARY

YELLOW

is-A

owns

TWEETY

SYLVESTER

 CAN-FLY (CANARY)

 CAN-FLY (TWEETY)

 SYLVESTER owns Something that can fly

 TWEETY is YELLOW

 SYLVESTER owns a CANARY

 SYLVESTER owns a BIRD


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Contradictions in Semantic Nets

color

CANARY YELLOW

SAMGREEN

is-A

color

  • Inconsistencies in SN‘s ? Pro and Con!

  • Operations on SN‘s  Non-monotonic Reasoning


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Semantic Networks. Semantics

Just a Graphical Representation of the Predicate Calculus ?

is-A

TWEETY

BIRD

Is-A (TWEETY, BIRD)


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Naïve Network Theory: Semantics

IS-A

ROBIN

BIRD

NETWORK

PK1

Is-A(ROBIN, BIRD)

LISP

(is-A ROBIN BIRD)

  • Pidgin English Semantics?

    Infix: (ROBIN IS-A BIRD)

    English: ‚ROBIN is a bird‘


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Example: Problems of Semantics

has-part

BIRD

WINGS

is-A

is-A

CLYDE

ROBIN

is-A

studied-by

ENDANGEREDSPECIES

NATURALISTS

has-part(BIRD,WINGS)  has-part(ROBIN,WINGS)

Property(ENDANGERED SPECIES)  Property(CLYDE)

Do the naturalists study the special bird CLYDE ?


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Nice Properties of Semantic Networks

  • Inheritance of Attributes

  • Propagation of Parts

  • Concept Centered Representation

  • Semantic Distance

  • Procedural Semantics of SN‘s

    BUT: How to get a Declarative (Tarski) or Denotational Semantics of SN‘s?


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Semantics

Woods (1975): „What‘s in a Link“?

  • Question: What is the Semantics of Semantic Networks?

    - The Intuition of the Reader?

    - The LISP-Programs operating on the SN?

    compare: the early work on the semantics of

    programming languages

    Levesque & Mylopoulos (1977):

  • Procedural Semantics

    Cercone & Schubert (1979):

  • Translation into First Order Predicate Logic



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