Introduction to Artificial Intelligence

Assoc. Prof. Osama fathy

2Knowledge Acquisition: is the bottle neck of building Intelligent systems

3 is the process of acquiring the knowledge from human experts or other sources (e.g. books, manuals) to solve the problem.

Knowledge Acquisition (KA)

4Knowledge Sources Text books, reports, databases (data mining), case studies, empirical data and Personal Expertise.

5Selection of the ExpertAsk the organization to provide you with the names of candidate domain experts, that is, those individuals who are believed to have significant expertise within the domain in question.

Select a domain expert whose performance is generally acknowledged to be above and beyond that of most others performing the same task.

Select an expert with a successful track record over a period of time.

6Selection of the Expert Select an expert who is both willing and able to

communicate personal knowledge, and who is relatively articulate in doing so.

Select an expert who is both willing and able to devote the time necessary to support the development effort.

If no expert can be identified, or made available, consider the development of the rule base through alternative means (as will be discussed in the sections to follow)

7Interviewing the Expert

a- There are 4 reasons why K. cant be satisfiedto describe the domain

For some problems there may not be an expert, Some experts are unable to describe what they

do, Some experts may not reveal their tricks of the

trade, and Some experts may actually have poor expertise

8b- Initial meeting has some purposes Relax individual (expert) Explain Problem Schedule follow-on meeting Evaluate the true extent of expertise of our

systems

Interviewing the Expert

9Interviewing the Expertc- Documentation

Date, time and location of the meeting Name of Expert (i.e., if more than one expert

is being used) List and description of the rules identified

during the meeting Listing of any new objects, attributes and/or

values encountered - and their properties

Documentation of the results of the meeting as soon as possible, it should contains such facts:

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Interviewing the Expert Identification of any new outside sources and

references Listing of any new terminology encountered,

and associated definitions Listing and discussion of any gaps or

discrepancies encountered Reminders (e.g., of points that need to be

clarified)

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Techniques used for extracting Knowledge from a domain experta- On-site observation

K.E. Watches D.E.

Note: difficulties are that the K.E. extracts the rules of solving the problems without expansion from D.E.

K.E watches the domain expert (D.E.) solving real problems on the job.

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Techniques used for extracting Knowledge from a domain expertb- Problem discussion

K.E. Explores D.E.

K.E. explores the kinds of data, K. and procedures needed to solve the problem through the discussion.

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Techniques used for extracting Knowledge from a domain expertc- Problem description

have the expert describes a prototypical problem for each category of answer in the domain.

Describes Prototype K.E.

D.E.

Note: difficulties are that the D.E. describes a prototype (not real problem).

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Techniques used for extracting Knowledge from a domain expertd- Problem analysis

K.E. presents the expert with a series of realistic problems then the D.E solves the problems with reasoning steps (analyze the results).

Describes Presents real problems solve with reasoning K.E. D.E.

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Techniques used for extracting Knowledge from a domain experte- System refinement

Have the D.E. gives the K.E. a series of problems to solve using the rules acquired from the interviews.

Gives the K.E. a series of problems to solveD.E. K.E.

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Techniques used for extracting Knowledge from a domain expertf- System examination

Have the D.E. examine and critique the prototype systems rules and control structure.

Examine and CritiqueD.E. K.E.

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Techniques used for extracting Knowledge from a domain expertg- System validation

Present the cases solved by D.E. and prototype system to other outside domain experts.

Examine and ValidateD.E. (s) K.E.

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Example: Identification ProblemConsider aircraft identification problem, draw thedecision tree and find the produced production rules according to the following order:

Engine type. Wing shape. Wing position. Tail shape. Bulges.

Number of engines Size and dimensions Color and markings Speed and altitude

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Attribute/Type C130 C141 C5A B747Engine type Propeller Jet Jet Jet

Wing position High High High LowWing shape Conventional Swept-back Swept-back Swept-backTail shape Conventional T-tail T-tail Conventional

Bulges Under-wing Aft-wing None Aft-cock pit

Assuming that we feel relatively confident with the filtered set of attributes, we may list these and their associated values.

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Solution-1

1- Decision TreeIf the attributes are selected as follows:

Engine type Wing position Wing shape Tail shape Bulges

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R1: IF X1 is Propeller THEN C130R2: IF X1 is Jet AND X2 is Low THEN B747R3: IF X1 is Jet AND X2 is high AND X3 is Conventional

THEN UnknownR4: IF X1 is Jet AND X2 is high AND X3 is Swept-back AND

X4 is Conventional THEN UnknownR5: IF X1 is Jet AND X2 is high AND X3 is Swept-back AND

X4 is T-tail AND X5 is Under-wing THEN UnknownR6: IF X1 is Jet AND X2 is high AND X3 is Swept-back AND

X4 is T-tail AND X5 is None THEN C5AR7: IF X1 is Jet AND X2 is high AND X3 is Swept-back AND

X4 is T-tail AND X5 is Aft-cockpit THEN UnknownR8: IF X1 is Jet AND X2 is high AND X3 is Swept-back AND

X4 is T-tail AND X5 is Aft-wing THEN C141

2- Production Rules

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Solution-2

R1: IF Bulges Under-wing THEN C130R2: IF Bulges is None THEN C5AR3: IF Bulges is Aft-cockpit THEN B747R4: IF Bulges is Aft-wing THEN C141

2- Production Rules:

1- Decision Tree:If the Bulges attribute only is selected