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Knowledge Representation Representational adequacy declarative, procedural Inferential adequacy manipulate knowledge incorporate new knowledge
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Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Dec 23, 2015

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Leon Cole
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Page 1: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Knowledge Representation

Representational adequacy declarative, procedural

Inferential adequacy manipulate knowledge incorporate new knowledge

Page 2: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Types of Knowledge

Simple factsComplex organized knowledgeprocedure - how to knowledgemeta-knowledge

Page 3: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Semantic Data ModelsHigh level model of model of

conceptual modelNot tied to implementation concernsFocus on

expressiveness simplicity concise formality

Page 4: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Semantic Nets

Nodes represent ObjectsLinks or Arcs represent Relationships

“instance of” - set membership “is a” - inheritance “ has a” - attribute descriptors “part of” - aggregation

Page 5: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Has a

Part-of

Instance of

Is a

Page 6: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Semantic NetsAdvantages Disadvantages

Flexibleeasy to understandsupport inheritance“natural” way to

represent knowledge

Hard to deal with exceptions

procedural knowledge difficult to represent

no standards for defining nodes or relationships

Page 7: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

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

Page 8: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Classes

Specify common properties of instancessupport hierarchical classificationsuperclass / subclass

subclass may be more refined version each subclass inherits operations and

attributes of its ancestors subclass may have its own operations and

attributes

Page 9: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Objects or Instances

Refers to things identified in model of conceptual model may be tangible (equipment, part,

orders, squashed bananas) may be mental constructs

Page 10: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Class vs instances

instances

Page 11: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Inheritance

Sharing attributes and behaviors within a class of objects

Person

customer

Employee

SalesPerson Manager

Sale Manager

Page 12: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Encapsulation

Attributes and behaviors (methods) integrated with the classes and objects

Attributes:size, location, appearance

Page 13: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Polymorphism

Each object responds in its unique way to messagesWhen changed method

When needed method

Page 14: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Object-Orientation

Tool for managing complexityemphasis on object structurespecify “what is”mapped directly from semantic net

Page 15: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Rule Representations

Rules are called productionsRule 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

Page 16: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

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

Page 17: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

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

Page 18: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Rule for understanding

Quantitative to Qualitative qualitative language is easier to

understand interpretation of numerical data make user feel comfortable with decision

logicIf temperature > 200 and humidity is

85% then machine is slightly overheated

Page 19: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Definitional Rules

Help communicate and train usersHelp 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

Page 20: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Rules support Generalizations

Allow reasoning with from specialization to generalizations

Support classification of objects at higher levels

Support refinements

Page 21: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

If pump operation temperature is over 300AND water mixture pH > 5.2THEN replace pump bearing and oil

Surface KnowledgeSurface Knowledge

•Hard to understand•Difficult to learn reasoning strategies•hard to update and expand 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

Page 25: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

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

Page 26: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Programming by descriptiondescribe the problem’s factsbuilt in inference engine combines

and uses facts and rules to make inferences

Predicate Logic

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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)

Page 28: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Frames

Similar to objectshelps organize entitiespackages operations (demons)easy to modifyextensible through inheritance

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Mammal Frame

Slot Values Default Demons

Skin Fur

Birth Live

Legs 4

Weight Computedemon

Page 30: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Frame - natural representation

Can accommodate a taxonomy of knowledge

contains defaults expectationsrepresent procedural and declarative

knowledge

Page 31: Knowledge Representation zRepresentational adequacy ydeclarative, procedural zInferential adequacy ymanipulate knowledge yincorporate new knowledge.

Facets Inference Value Prompt

Exhaustive Conf

SearchOrder

Default Expand

WhenChanged

Init QueryFrom

WhenNeeded

Reinit Unknown

Facets - properties of slots