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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

Part IIMethods of AI

Chapter 3

Knowledge and Reasoning

slide2

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

slide3

3.5 Knowledge

Representation

Semantic Networks

slide4

Semantic

Networks

slide5

Simple and

Naive

Semantic

Nets

naive semantic networks to get started
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

slide7

Semantic Nets:

an „unstructured“ Example

PROBLEM-1: Semantics of the SN?

PROBLEM-2: Structure of the SNs ?

Problem-3: Binary relations?

semantic networks 1 sch nfinkeln
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:

semantic networks an example for an n ary relation
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

token type distinction in sn s
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”

semantic networks platon s world
Semantic Networks: Platon´s World

Generic Concept: An Arch

  • Generic Concept
  • Individual Concept
  • Individual Object:

PARIS

An Arc de Triomphe

inferences with semantic nets
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

contradictions in semantic nets
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
semantic networks semantics
Semantic Networks. Semantics

Just a Graphical Representation of the Predicate Calculus ?

is-A

TWEETY

BIRD

Is-A (TWEETY, BIRD)

na ve network theory semantics
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‘

example problems of semantics
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 ?

nice properties of semantic networks
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?

semantics
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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