Lecture12.pptArtificial intelligence for decision support systems

Artificial intelligence for decision support systems

1. ARTIFICAL
INTELLIGENCE
AND EXPERT
SYSTEMS
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Part3

2. (12.7) How ES Work: Inference Mechanisms
•Development process of ES
–Is the process for eliciting knowledge from experts and
then storing the knowledge in the knowledge base.
–A typical process for developing ES includes:
•knowledge acquisition
•Knowledge representation
•Selection of development tools
•System prototyping
•Evaluation
•Improvement
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3. (12.8) Problem Areas Suitable for ES
•Interpretation
•Prediction
•Diagnosis
•Design
•Planning
•Monitoring
•Debugging
•Repair
•Instruction
•Control
Generic categories of ES
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4. (12.9) Development of ES
•The development of ES includes:
1.Defining the nature and scope of the problem
–Rule-based ES are appropriate when the
nature of the problem is qualitative, knowledge
is explicit, and experts are available to solve
the problem effectively and provide their
knowledge
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5. (12.9) Development of ES
•The development of ES includes:
2.Identifying proper experts
–A proper expert should have a thorough
understanding of:
•Problem-solving knowledge
•The role of ES and decision support technology
•Good communication skills
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6. (12.9) Development of ES
•The development of ES includes:
3.Acquiring knowledge
–Knowledge engineer
An AI specialist responsible for the technical
side of developing an expert system. The
knowledge engineer works closely with the
domain expert to capture the expert’s
knowledge in a knowledge base
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7. (12.9) Development of ES
•The development of ES includes:
4.Selecting the building tools
–There are three different kinds of
development tools:
a)General-purpose development environment
b)Expert system shell
A computer program that facilitates relatively
easy implementation of a specific expert
system. Analogous to a DSS generator
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8. (12.9) Applications of ES
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9. (12.9) Development of ES
There are three different kinds of development tools
(cont.):
c)Tailored turn-key solutions
•Contain specific features often required for
developing applications in a particular domain
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10. (12.9) Development of ES
•The development of ES includes:
5.Choosing an ES development tool
–Consider the cost benefits
–Consider the technical functionality and
flexibility of the tool
–Consider the tool's compatibility with the
existing information infrastructure
–Consider the reliability of and support from the
vendor
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11. (12.9) Development of ES
•The development of ES includes:
6.Coding the system
–The major concern at this stage is whether the
coding process is efficient and properly
managed to avoid errors
7.Evaluating the system
–Two kinds of evaluation:
•Verification: no error in the code and achieves
results the same as that acquired by the expert
•Validation: solve the problem correctly
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12. (12.9) Benefits, Limitations, and Success
Factors of ES
•Benefits of ES
–Increased output and productivity
–Decreased decision-making time
–Increased process and product quality
–Reduced downtime
–Capture of scarce expertise
–Flexibility
–Easier equipment operation
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13. (12.9) Benefits, Limitations, and Success
Factors of ES
•Benefits of ES
–Elimination of the need for expensive
equipment
–Operation in hazardous environments
–Accessibility to knowledge and help desks
–Ability to work with incomplete or uncertain
information
–Provision of training
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14. (12.9) Benefits, Limitations, and Success
Factors of ES
•Benefits of ES
–Enhancement of problem solving and decision
making
–Improved decision-making processes
–Improved decision quality
–Ability to solve complex problems
–Knowledge transfer to remote locations
–Enhancement of other information systems
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15. (12.9) Benefits, Limitations, and Success
Factors of ES
•Problems with ES
–Knowledge is not always readily available
–It can be difficult to extract expertise from humans
–The approach of each expert to a situation assessment
may be different yet correct
–It is difficult to abstract good situational assessments
when under time pressure
–Users of ES have natural cognitive limits
–ES work well only within a narrow domain of
knowledge
–Most experts have no independent means of checking
whether their conclusions are reasonable
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16. (12.9) Benefits, Limitations, and Success
Factors of ES
•Problems with ES
–The vocabulary that experts use to express facts and
relations is often limited and not understood by others
–ES construction can be costly because of the expense
of knowledge engineers
–Lack of trust on the part of end users may be a barrier
to ES use
–Knowledge transfer is subject to a host of perceptual
and judgmental biases
–ES may not be able to arrive at conclusions in some
cases
–ES sometimes produce incorrect recommendations
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17. (12.9) Benefits, Limitations, and Success
Factors of ES
•Factors in disuse of ES
–Lack of system acceptance by users
–Inability to retain developers
–Problems in transitioning from development to
maintenance
–Shifts in organizational priorities
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18. (12.9) Benefits, Limitations, and Success
Factors of ES
•ES success factors
–Level of managerial and user involvement
–Sufficiently high level of knowledge
–Expertise available from at least one
cooperative expert
–The problem to be solved must be mostly
qualitative
–The problem must be sufficiently narrow in
scope
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19. (12.9) Benefits, Limitations, and Success
Factors of ES
•ES success factors
–The ES shell must be of high quality and
naturally store and manipulate the knowledge
–The user interface must be friendly for novice
users
–The problem must be important and difficult
enough to warrant development of an ES
–Knowledgeable system developers with good
people skills are needed
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20. (12.9) Benefits, Limitations, and Success
Factors of ES
•ES success factors
–End-user attitudes and expectations must be
considered
–Management support must be cultivated
–End-user training programs are necessary
–The organizational environment should favor
adoption of new technology
–The application must be well defined,
structured, and it should be justified by
strategic impact
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21. ES on the Web
•The relationship between ES and the
Internet and intranets can be divided into
two categories:
–The Web supports ES (and other AI)
applications
–The support ES (and other AI methods) give to
the Web
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