Contents

Knowledge Acquisition

Machine Learning

Knowledge Acquisition

The transfer and transformation of potential problem solving

expertise from some knowledge source to a program.

Buchanan, 1983

Knowledge Acquisition

The process of acquiring, studying and organizing

knowledge, so that it can be used in a knowledge-based

system.

Expert may provide irrelevant, incomplete or inconsistent

information.

Data and knowledge acquisition

Collect and analyze data and knowledge

Make key concepts of the system design more explicit

Knowledge Acquisition (Cont.)

Acquired knowledge may consist facts, rules, concept,

procedures, heuristics, formulas, relationships, statistics, or

other useful informationSources

DocumentedWritten, viewed, sensory, behavior

UndocumentedMemory

Acquired fromHuman sensesMachines

Knowledge

Levels Shallow

Surface levelInput-output

Deep Problem solvingDifficult to collect, validateInteractions between system components

Knowledge

CategoriesDeclarative

Descriptive representationProcedural

How things work under different circumstancesHow to use declarative knowledge

Problem solvingMeta knowledge

Knowledge about knowledge

Knowledge EngineersProfessionals who elicit knowledge from experts

Empathetic, patient

Broad range of understanding, capabilities

Integrate knowledge from various sourcesCreates and edits code

Operates tools

Build knowledge baseValidates information

Trains users

Knowledge Acquisition Difficulties

Problems in Transferring Knowledge

Expressing KnowledgeTransfer to a MachineNumber of ParticipantsStructuring Knowledge

Experts may lack time or not cooperate

Testing and refining knowledge is complicated

Poorly defined methods for knowledge elicitation

System builders may collect knowledge from one source, but the relevant knowledge may be scattered across several sources

May collect documented knowledge rather than use experts

The knowledge collected may be incomplete

Difficult to recognize specific knowledge when mixed with irrelevant data

Other Reasons

Overcoming the Difficulties Knowledge acquisition tools with ways to decrease the representation mismatch between the human expert and the program (“learning by being told”)

Simplified rule syntax

Natural language processor to translate knowledge to a specific representation

Impacted by the role of the three major participants

Knowledge Engineer

Expert

End user

Critical

The ability and personality of the knowledge engineer

Must develop a positive relationship with the expert

The knowledge engineer must create the right

impression

Computer-aided knowledge acquisition tools

Extensive integration of the acquisition efforts

Overcoming the Difficulties

Knowledge Acquisition Methods

Manual

Semiautomatic

Automatic (Computer Aided)

Manual Methods - Structured Around Interviews

Process (Figure next slide)InterviewingTracking the Reasoning Process ObservingManual methods: slow, expensive and sometimes inaccurate

Manual Methods of Knowledge Acquisition

Elicitation

Knowledgebase

Documentedknowledge

Experts

CodingKnowledgeengineer

Semiautomatic Methods

Support Experts Directly (Figure next slide)

Help Knowledge Engineers

Expert-Driven Knowledge Acquisition

Knowledgebase

Knowledgeengineer

Expert CodingComputer-aided(interactive)interviewing

Automatic Methods

Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated)

Induction Method (Figure next slide)

Induction-Driven Knowledge Acquisition

Knowledgebase

Case historiesand examples

Inductionsystem

Machine Learning

Machine learning is a specialized form of autonomous

knowledge acquisition.

Autonomous knowledge creation or refinement through

the use of computer programs.

Why is Machine Learning Important?

Some tasks cannot be defined well, except by examples (e.g.,

recognizing people).

Relationships and correlations can be hidden within large

amounts of data. Machine Learning/Data Mining may be able to

find these relationships.

Human designers often produce machines that do not work as

well as desired in the environments in which they are used.

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The amount of knowledge available about certain tasks might be

too large for explicit encoding by humans (e.g., medical

diagnostic).

Environments change over time.

New knowledge about tasks is constantly being discovered by

humans. It may be difficult to continuously re-design systems “by

hand”.

Why is Machine Learning Important?

Types of Learning Learning by memorization

Direct instruction

Analogy

Induction

Deduction

Learning by Memorization

It requires the least amount of inference and is

accomplished by simply copying the knowledge in the

same form that it will be used directly into the

knowledge base.

Learning by Direct Instruction

The knowledge must be transformed into an

operational form before being integrated into the

knowledge base

This type of learning used when a teacher presents

a number of facts directly to us in a well organized

manner.

Analogical Learning

Is the process of learning a new concept or solution

through the use of similar known concepts or solutions.

Here, previously learn examples serve as a guide.

Driving a truck using experience of driving a car.

Learning by Induction

This form of learning requires the use of inductive

inference

We use inductive learning when we formulate a

general concept after seeing a number of instances

or examples of the concept.Example:

we learn the concepts of color or sweet taste after experiencing the sensations associated with several examples of color example objects or sweet foods

Deductive Learning

It is accomplished through a sequence of deductive

inference steps using known facts.

From the known facts, new facts and relationships

are logically derived.

Example:(father X of Y), (father Y of Z);

(Grandfather X of Z)

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