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Knowledge Representation

Knowledge Representation. Representational adequacy declarative, procedural Inferential adequacy manipulate knowledge incorporate new knowledge. Types of Knowledge. Simple facts Complex organized knowledge procedure - how to knowledge meta-knowledge. Semantic Data Models.

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Knowledge Representation

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  1. Knowledge Representation • Representational adequacy • declarative, procedural • Inferential adequacy • manipulate knowledge • incorporate new knowledge

  2. Types of Knowledge • Simple facts • Complex organized knowledge • procedure - how to knowledge • meta-knowledge

  3. Semantic Data Models • High level model of model of conceptual model • Not tied to implementation concerns • Focus on • expressiveness • simplicity • concise • formality

  4. Semantic Nets • Nodes represent Objects • Links or Arcs represent Relationships • “instance of” - set membership • “is a” - inheritance • “ has a” - attribute descriptors • “part of” - aggregation

  5. Is a Has a Part-of Instance of

  6. Flexible easy to understand support inheritance “natural” way to represent knowledge Hard to deal with exceptions procedural knowledge difficult to represent no standards for defining nodes or relationships Semantic NetsAdvantages Disadvantages

  7. Classes, Objects, Attributes, Values - Object Orientation • Classes describe common properties of objects • Objects may be physical or conceptual • Attributes are characteristics of objects • Values are specific measures of Attributes for specific instances

  8. Classes • Specify common properties of instances • support hierarchical classification • superclass / subclass • subclass may be more refined version • each subclass inherits operations and attributes of its ancestors • subclass may have its own operations and attributes

  9. Objects or Instances • Refers to things identified in model of conceptual model • may be tangible (equipment, part, orders, squashed bananas) • may be mental constructs

  10. Class vs instances Person class instances

  11. Inheritance • Sharing attributes and behaviors within a class of objects Person Employee Sales Person Manager customer Sale Manager

  12. Encapsulation • Attributes and behaviors (methods) integrated with the classes and objects Attributes: size, location, appearance Methods

  13. Polymorphism • Each object responds in its unique way to messages When changed method When needed method

  14. Object-Orientation • Tool for managing complexity • emphasis on object structure • specify “what is” • mapped directly from semantic net

  15. Rule Representations • Rules are called productions • Rule have two parts • condition part, premise -> IF • action part ,conclusion-> THEN • The action can add a fact to the knowledge base, start a procedure or display a screen

  16. Rules represent knowledge • Apply O-A-V framework (object-attribute-value) • IF air vehicle is a plane AND plane maximum altitude is 40000 AND plane manufacturer is Boeing THEN ASK Flight Display 15

  17. Representing knowledge • Abstracting with rules • translate quantitative to qualitative • define technical terms • support generalized reasoning • make rules for user • easy to understand • help user follow decision logic

  18. Rule for understanding • Quantitative to Qualitative • qualitative language is easier to understand • interpretation of numerical data • make user feel comfortable with decision logic • If temperature > 200 and humidity is 85% then machine is slightly overheated

  19. Definitional Rules • Help communicate and train users • Help user understand vocabulary • Promotes common agreement on terms for expert, user and knowledge engineer • IF you want more than one source file of classes THEN use package keyword

  20. Rules support Generalizations • Allow reasoning with from specialization to generalizations • Support classification of objects at higher levels • Support refinements

  21. Surface Knowledge • Hard to understand • Difficult to learn reasoning strategies • hard to update and expand knowledge base If pump operation temperature is over 300 AND water mixture pH > 5.2 THEN replace pump bearing and oil

  22. Hierarchical Classification Abstraction draws out important aspects Solution abstractions Feature abstractions Heuristic Match generalize refine Features Recommendations

  23. Deep knowledge Lubrication defect Is a Poor Oil Viscosity causes causes Hot Pump Low Temp temperature is over 300 water mixture pH > 5.2

  24. Reasoning at higher level requires Lubrication defect Maintenance Type of Fix heat damage Remedy Replace bearing and oil

  25. Modular style - easy to add, update and delete natural for many problem domains uncertain knowledge may be represented May be difficult to understand may demonstrate unpredictable behavior extra effort required to representing structural knowledge Rules Advantages Disadvantages

  26. Predicate Logic • Programming by description • describe the problem’s facts • built in inference engine combines and uses facts and rules to make inferences

  27. Prolog Programming • Declaring facts about objects and their relationships -> likes (john,mary) • Defining rules about objects and relationships • Asking Questions about objects sister-of(X,Y) :- female(X), parents(X,M,F), parent(Y,M,F)

  28. Frames • Similar to objects • helps organize entities • packages operations (demons) • easy to modify • extensible through inheritance

  29. Mammal Frame

  30. Frame - natural representation • Can accommodate a taxonomy of knowledge • contains defaults expectations • represent procedural and declarative knowledge

  31. Facets - properties of slots

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