Knowledge Acquisition

• Types of Knowledge

• Knowledge Acquisition Paradox

• Difficulties of Knowledge Acquisition

• Knowledge Acquisition Methods

• Automatic Knowledge Acquisition Technology

Types of Knowledge• Declarative Knowledge - tells us the facts

– Facts, knowledge about objects and relationships– Descriptive representation of knowledge– It is often shallow knowledge

Procedural Knowledge - tells us what to do– Knowledge about procedures involved in solving

problems Declarative Knowledge - tells us facts and

procedural knowledge tells us what to do

Knowledge Acquisition Paradox

The more competent a Domain Expert (DE) becomes, the less able they are to describe the knowledge they use to solve problems

Don’t be your own expert

Don’t believe everything experts say

Difficulties of Knowledge Acquisition

• Difficulty in verbalizing– Reasoning process too broad– Use of combined and compiled knowledge– Unaware of the individual steps taken to reach a

solution

• Difficulties in transferring to a machine– Machine works at a more basic level, but the

expert seldom operates at a basic level

Difficulties of Knowledge Acquisition

• Difficulties in structuring knowledge– Losing a significant amount of knowledge

when structuring implicit knowledge

• Domain Expert’s unwillingness– Unavailable– Uncooperative– No knowledge of computers and Expert

Systems

Knowledge Acquisition Methods 1

On-site observation Watch the expert solving real problems on the

job• We are not the experts, so we research the particular

area BEFORE sitting down with the Domain Expert(s)

• Ex: Sometimes a Doctor brings a Student with them/Student learns from the Expert

Knowledge Acquisition Methods 2

Problem discussion - observe at first Explore the kinds of data, knowledge, and procedures

needed to solve specific problems How does the problem differ from prototypical problems in

the domain? How is this problem different from others? What different approach do you use?

Types of data required and kinds of solutions adequate for the problem?

What kinds of knowledge are needed to solve the problem? What constitutes an adequate explanation or justification of

a problem solution?

Knowledge Acquisition Methods 3 Problem Description

Have the expert describe a prototypical problem for each category of answer in the domain

Protocol Analysis (Problem Analysis) Present the expert with a series of realistic

problems to solve aloud, probing for the rationale behind the reasoning steps (solve the problem verbally)

Widely used in psychology Ex: Dermatology-Psoriasis

Expert Syst. to diagnose Psoriasis Color? How long rash lasts? Where is the rash?

Knowledge Acquisition Methods 4

• Repertory Grid Analysis– Identify important objects– Identify important attributes

• Specific objects– Example: Rash/Color/Duration/Level of itching/Local

or whole body?

– For each attributes, establish a bipolar scale with differentiable characteristics and their opposites

– Ex: Computer Language

Repertory Grid Analysis• Assisting in selecting a computer language

– Identify objectives • LISP, C (Procedural Lang), C++(OOP Lang)

– Attributes• Availability, Ease of Programming, Training

Time• Orientation

– Traits• high, low, symbolic, numeric

Reasoning Methods• Deductive Reasoning

• Inductive Reasoning

• Forward Reasoning (Chaining)– Reasoning starts with raw facts

• Backward Reasoning (Chaining)– Reasoning starts with hypothesis as in

statistics, them moves to prove or disprove hypothesis

RGA Input for Selecting a Computer Language

Attributes: Trait or Opposite

Availability: Widely Available or Not Available

Ease of Programming: High or Low(C++) (C)

Training Time: Low or High

Orientation: Symbolic or Numeric

Example: The Animal Problem – Done in LISP – “Symbol OrientedCan store colors – Red/Blue/Orange/Green1 variable can be 26 Char long/1 char long

Automatic Knowledge Acquisition Techniques

• Methods– Rule Induction - DE provides some examples

similar to Data Mining, then apply Statistical/Mathematical Techniques such as Multivariate Regression

– Artificial Neural Net (ANN) - Qualitative Approach-Statistical & Mathematical Methods/Dev. Intelligent Machine/Data Mining

Automatic Knowledge Acquisition Techniques

• Methods– Case-based Reasoning - asking DE to provide

case/Law - Attorney• Work by previous cases/Dev. argument from

previous cases

• Use previous as base argument– Example: Help Desk

» Printer not functioning

» Refer to previous case from “n” weeks ago

Automatic Knowledge Acquisition Techniques

• Methods– Model-based Reasoning

• Applicable to design of an engineering application

• Give me specifications of some hardware

• Used often in NASA

• Build a model using DE knowledge

Knowledge Representation

• Logic is used heavily in AI– Prepositional Logic– Predicate Logic– Rules (easiest to represent)– Semantic Nets– Frame– Object

Propositional Logic

• It is raining– RAINING

• Proposition/Propositional Logic - Is this true or false? Is it raining now?

• It is sunny

• We can deduce whether a certain proposition (fact) is true or false

Proposition Logic• Propositional logic cannot drive the

association• Socrates is a man (true or false)

– SOCRATESMAN• Plato is a man (true or false)• We can not draw any conclusions about

similarities between Socrates and Plato– By separate propositional logic cannot

reach a conclusion• Variable = Substituting a value• Constant = Have to assign value

Predicate Logic

• More like a variable/can hold different values• Socrates is a man (true or false)

– PREDICATE(VALUE)• Socrates is a man

– MAN(SOCRATES)• Plato is a man

– MAN(PLATO)• Now the structure of representation reflects

the structure of knowledge itself

Predicate Logic

• Marcus is a man– MAN(Marcus)

• Marcus is a Pompeian– POMPEIAN (Marcus)

• All Pompeians were Romans– Vx POMPEIAN(x) -> ROMAN(x)

Predicate Logic

• All Romans were either loyal to Caesar or hated him

• Vx ROMAN(x) -> loyalto (x, Caesar) v hate (x, Caesar)

• It is difficult to represent knowledge in predicate logic

Rules

If

(conditions)

Then

(actions)

Else

(actions)

Semantic Networks (Nets)

• Semantic net is a knowledge presentation method based on a network structure

• It consists of – points called nodes connected by– links called arcs

• Nodes – object, concepts, events

• Arcs - relationships between nodes

Semantic Nets

• Common arcs used for representing hierarchies include isa and has-part

• Processing Natural Language– Example: Text Mining

• Uses Natural Language for summarizing article or newspaper

Example: The Queen Mary is an ocean linerEvery ocean liner is a ship

Ship

Ocean Liner

Queen Mary

isa

isa

SHIP

HullEngineOil TankerOcean Liner

BoilerLiver PoolQueen MarySwimming Pool

Isa (hierarchical relationship)Has-part (component relationship)

isa

Bill gives Judy a gift

Judy

Give(verb)

Gift

Bill

Object

Recieves

Gives

Node

Node

Bill told Laura that he gave Judy a gift

Judy

Give(verb)

Gift

Bill

Object

Recieves

Gives

Tell

Laura

Speaker

Listener

TimePast

Frame 1• Similar to Object• Hierarchical Representation

– Introduce details as necessary– Polymorphism– Multi-inheritance

• A data structure for representing a stereotyped situation

• A network of nodes and relations organized in a hierarchy

• The topmost nodes - general concepts (abstract class)

• The lower nodes - more specific instances (more specific classes)

Frame 2

• The concepts at each node is described by a set of attributes and values of those attributes

• Attributes are called slots• Each slot can have procedures (codes)• Typical procedures

– if added procedure– if deleted procedure– if needed procedure

Frame 3

• OOP– Class– Attribute– Method

• AI– Node– Slots– Procedures

Report

Technical ReportProgress Report

DSS Project Process Report

isa isa

isa

A Node in a Frame System

Value 1

Value 3

Value 2Slot 2

Slot 3

Slot 1

Procedure 2

Procedure 1

Procedure 3

Comparisons of KR Methods• Rules• When get too large become unmanageable

– IF… THEN… ELSE– Advantage

• Simple syntax, easy to understand, simple interpreter, high modularity, flexible

– Disadvantage• Hard to follow hierarchies, inefficient for large

systems, not all knowledge can be expressed as rules

Comparisons of KR Methods

• Semantic Nets– Advantage

• Easy to follow hierarchy, easy to trace association, flexible

– Disadvantage• Meaning attached to nodes might be

ambiguous• Exception handling is difficult• Difficult to program

Comparisons of KR Methods

• Frames– Advantage

• Expressive power, easy to set up slots for new properties and relations

• Easy to create specialized procedures• Easy to include default information and detect

missing values

– Disadvantage• Difficult to program• Difficult for inference