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Part II Methods of AI

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

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  1. Part IIMethods of AI Chapter 3 Knowledge and Reasoning

  2. 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

  3. 3.5 Knowledge Representation Semantic Networks

  4. Semantic Networks

  5. Simple and Naive Semantic Nets

  6. 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

  7. Semantic Nets: an „unstructured“ Example PROBLEM-1: Semantics of the SN? PROBLEM-2: Structure of the SNs ? Problem-3: Binary relations?

  8. Another famous Example: Winston´s Arch

  9. 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:

  10. 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

  11. 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”

  12. Semantic Networks: Platon´s World Generic Concept: An Arch • Generic Concept • Individual Concept • Individual Object: PARIS An Arc de Triomphe

  13. 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

  14. 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

  15. Semantic Networks. Semantics Just a Graphical Representation of the Predicate Calculus ? is-A TWEETY BIRD Is-A (TWEETY, BIRD)

  16. 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‘

  17. 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 ?

  18. 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?

  19. 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

  20. Distinction of Representational and Conceptual Levels in Semantic Networks

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