Download - 001.Intro to Knowledge Representation
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Knowledge Representation
Representational adequacydeclarative, procedural
Inferential adequacymanipulate knowledgeincorporate new knowledge
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Types of Knowledge
Simple factsComplex organized knowledgeprocedure - how to knowledgemeta-knowledge
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Semantic Data Models
High level model of model of conceptualmodelNot tied to implementation concernsFocus on
expressivenesssimplicityconciseformality
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Semantic Nets
Nodes represent ObjectsLinks or Arcs represent Relationships
instance of - set membership is a - inheritance has a - attribute descriptors part of - aggregation
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Has a
Part-of
Instance of
Is a
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Semantic Nets
Advantages DisadvantagesFlexibleeasy to understand
support inheritance natural way torepresent knowledge
Hard to deal withexceptions
procedural knowledgedifficult to representno standards fordefining nodes orrelationships
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Classes, Objects, Attributes,Values - Object Orientation
Classes describe common properties ofobjects
Objects may be physical or conceptual Attributes are characteristics of objects Values are specific measures of Attributesfor specific instances
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Classes
Specify common properties of instancessupport hierarchical classificationsuperclass / subclass
subclass may be more refined versioneach subclass inherits operations and
attributes of its ancestorssubclass may have its own operations and
attributes
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Objects or Instances
Refers to things identified in modelof conceptual model
may be tangible (equipment, part,orders, squashed bananas)may be mental constructs
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Class vs instances
instances
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Inheritance
Sharingattributes and
behaviorswithin a class ofobjects
Person
customer
Employee
SalesPerson Manager
Sale Manager
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Encapsulation
Attributes and behaviors (methods)integrated with the classes and objects
Attributes:size, location,
appearance
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Polymorphism
Each object responds in its unique way tomessages When changed method
When needed method
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Object-Orientation
Tool for managing complexityemphasis on object structurespecify what is mapped directly from semantic net
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Rule Representations
Rules are called productionsRule have two parts
condition part, premise -> IFaction part ,conclusion-> THEN
The action can add a fact to theknowledge base, start a procedure ordisplay a screen
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Rules represent knowledge
Apply O-A-V framework (object-attribute-value)
IF air vehicle is a plane AND planemaximum altitude is 40000 AND planemanufacturer is Boeing THEN ASK Flight
Display 15
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Representing knowledge
Abstracting with rulestranslate quantitative to qualitative
define technical termssupport generalized reasoning
make rules for user
easy to understandhelp user follow decision logic
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Rule for understanding
Quantitative to Qualitativequalitative language is easier to understand
interpretation of numerical datamake user feel comfortable with decision
logic
If temperature > 200 and humidity is85% then machine is slightly overheated
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Definitional Rules
Help communicate and train usersHelp user understand vocabularyPromotes common agreement on termsfor expert, user and knowledge engineerIF you want more than one source file ofclasses THEN use package keyword
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Rules supportGeneralizations
Allow reasoning with from specializationto generalizations
Support classification of objects at higherlevelsSupport refinements
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If pump operation temperature is over 300AND water mixture pH > 5.2THEN replace pump bearing and oil
Surface Knowledge
Hard to understand Difficult to learnreasoning strategies hard to update andexpand knowledge base
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Hierarchical Classification
Feature abstractions Solution abstractions
Features Recommendations
generalize
Heuristic Match
refine
Abstraction draws out important aspects
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Deep knowledge
Hot Pump Low Temp
Poor Oil Viscosity
Lubrication defect
causescauses
Is a
water mixture pH > 5.2temperature is over 300
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Reasoning at higher level
Lubrication defectrequires
Maintenance
Fix heatdamage
Replace bearingand oil
Type of
Remedy
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Modular style - easyto add, update anddeletenatural for manyproblem domainsuncertain knowledgemay be represented
May be difficult tounderstand
may demonstrateunpredictablebehaviorextra effort requiredto representingstructural knowledge
RulesAdvantages Disadvantages
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Programming by descriptiondescribe the problems facts
built in inference engine combines anduses facts and rules to make inferences
Predicate Logic
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Mammal Frame
Slot Values Default Demons
Skin Fur
Birth Live
Legs 4
Weight Computedemon
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Frame - naturalrepresentation
Can accommodate a taxonomy ofknowledge
contains defaults expectationsrepresent procedural and declarativeknowledge
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Facets Inference Value Prompt
Exhaustive Conf
SearchOrder
Default Expand
When
Changed
Init Query
FromWhenNeeded
Reinit Unknown
Facets - properties of slots