Download What`s an Expert System

CS 420 Expert Systems – 2015/2016
Handout: Lab 1
Lab Contents:
- Introduction To Expert Systems
- Expert Systems vs. Artificial Intelligence
- Expert Systems vs. Decision Support Systems
- ES Structure / Architecture
- ES Features
- ES Lifecycle
- Advantages and disadvantages of ES
Introduction to Expert Systems
What’s an Expert System
An interactive computer based decision tool that uses facts
 A computer system that emulates the decision-making ability of a human expert
in a restricted domain [Giarratano & Riley 1998]
 Edward Feigenbaum
– “An intelligent computer program that uses knowledge and inference
procedures to solve problems that are difficult enough to require
significant human expertise for their solutions.”
[Giarratano & Riley
1998]
 Sometimes, we also refer to knowledge-based system
WHY?
to solve difficult decision making problems, based on knowledge acquired from an expert
in a certain field
Expert Systems vs. Artificial Intelligence
 Expert systems: a computer application that employs a set of rules based on
human knowledge to solve problems that require human expertise
– Imitates reasoning of experts on “information fit”
– A non-expert simulates a dialog with an expert to solve complex problems
 Artificial Intelligence: practical mechanisms that enable computers to simulate the
human reasoning process
– Interface of compute science/cognitive psychology
– “the study of how to make computers do things which humans do better” –
the Turing Test?
Expert Systems vs. Decision Support Systems
Decision support system ( DSS ):
It is an information system that Provides its user with decision oriented information
whenever decision making situation arises
Eng. Nareeman Sabry | Eng. Amal Ibrahim
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Expert System ( ES ):
It is information system collect the knowledge from an expert in specific field to an
computer system
Decision support systems (DSS)
 DSS: an IS designed to help managers select one of many alternative solutions
 Components of a DSS
o Data management module: a database or data warehouse that holds and
maintains data for the DSS; data may come from a number of sources
including such systems as SCM (supply chain management) and CRM
o
o
o
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(customer relationship management)
Model management module: contains a model or models to be used by the
DSS; the model may be fixed (static), dynamic (able to change due to
changes in the data), or it may be a collection of possible models from
which the DSS or the user may select
Dialog module: this is the interface between the user and the DSS; this is
what the user would interact with to enter data, query the system, produce
reports, etc.
Sensitivity analysis module: this is used to determine what effect
particular parameters have on the result; for example, you may be doing
an analysis for a municipality where the amount of tax revenue generated
is given great weight toward the outcome
Examples of DSS given in text:
o Production and retailing: used to project purchasing trends and help decide
how much product to stock and where to purchase it
o Tax planning: used to make financial decisions to help reduce tax burden
o Web site planning and adjustment: used to analyze customer behavior and
suggest changes to design
o
o
o
Yield management: used to maximize overall revenue, often by using
price discrimination
Financial services: used to make decisions such as loan approvals
Benefit analysis: used to help determine which package of benefits is best
suited for someone's needs and budget
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Expert systems (ES)
 Expert systems emulate the knowledge of an expert in a narrow field/domain
 Ai: artificial intelligence; the name given to a broad field where computer
engineers and scientists try to mimic patterns of thinking and learning using
computers
 Knowledge base: a collection of facts and the relationships between them; much
of the content may be a collection of if/then rules
 Inference engine: software that combines data input by the user with a
knowledge base to try to suggest a solution
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Neural networks: hardware or software designed to mimic the way a brain works
Turing test: a test proposed by alan turing to determine if computers can think.
To pass the turing test, a computer must have a dialog with humans and have the
humans not be able to tell if they are talking to a computer or another person. To
make the test a little more achievable, the dialog is almost always carried out as a
text connection, like instant messaging or chat.
Intelligent agent: software designed to wait to perform particular operations
when triggered by a specific event, such as automatically reordering an item when
the stock level of that item falls below a certain value

Examples of es given in text:
o Medical diagnosis: used to recognize patterns of diseases based on test
results
o Medical management: used to suggest courses of action and help avoid
bad interactions with medicines, procedures, etc.
o Credit evaluation
o Detection of insider securities trading
o Detection of common metals
o Irrigation and pest management
o Diagnosis and prediction of mechanical failure

GDDS: Group Decision Support System
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ES Structure / Architecture
Components of an Expert System
ES Features
 Is limited to specific domain
 Is capable of explaining its own line of reasoning in a comprehensive way
 Has knowledge that is clearly separated from its program input data
 Can often reason with uncertain or incomplete information.
 Will delivers output as advice instead of just tables of figures.
 It must exhibit high performance in terms of speed and reliability in order to be a
useful tool.
 Is designed to grow incrementally.
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ES Lifecycle
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Problem Identification Phase: Identifying the problem and opportunity where the
organization can obtain benefits from an ES, and establishing the ES general
goals
Feasibility Study Phase: Assessing the feasibility of the ES development in terms
of its technical, economical, and operational feasibility.
Project Planning Phase: Planning for the ES project, including development
team members, working environment, project schedule, and budget.
Knowledge Acquisition Phase: Extracting domain knowledge from domain
experts and determining the system’s requirements.
Knowledge Representation Phase: Representing key concepts from the domain,
and interrelationships between these concepts, using formal representation
methods.
Knowledge Implementation Phase: Coding the formalized knowledge into a
working prototype.
Verification and Validation: Verifying and validating a working prototype
against the system’s requirement, and revising it as necessary according to
domain experts’ feedback.
Installation/Transition/Training: Installing the final prototype in an operating
environment, training the users, and developing documentation/user’s manual.
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
Operation/Evaluation/Maintenance Running the system in an operating
environment, evaluating its performance and benefits, and maintaining the
system.
Advantages and disadvantages of ES
ES Advantages:
 Economical
– Lower cost per user
 Availability
– Accessible anytime, almost anywhere
 Response time
– Often faster than human experts
 Reliability
– Can be greater than that of human experts
– No distraction, fatigue, emotional involvement, …
 Explanation
– Reasoning steps that lead to a particular conclusion
 Intellectual property
– Can’t walk out of the door
 Permanence - expert systems do not forget, but human experts may
 Reproducibility - many copies of an expert system can be made, but training new
human experts is time-consuming and expensive
 If there is a maze of rules (e.g. Tax and auditing), then the expert system can
"unravel" the maze
 Efficiency - can increase throughput and decrease personnel costs
– Although expert systems are expensive to build and maintain, they are
inexpensive to operate
– Development and maintenance costs can be spread over many users
– The overall cost can be quite reasonable when compared to expensive and
scarce human experts
– Cost savings:
wages - (elimination of a room full of clerks)
other costs - (minimize loan loss)
 Consistency - with expert systems similar transactions handled in the same way.
The system will make comparable recommendations for like situations.
Humans are influenced by
– Recency effects (most recent information having a disproportionate impact
on judgment)
– Primacy effects (early information dominates the judgment).
 Documentation - an expert system can provide permanent documentation of the
decision process
 Completeness - an expert system can review all the transactions, a human expert
can only review a sample
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Timeliness - fraud and/or errors can be prevented. Information is available sooner
for decision making
Breadth - the knowledge of multiple human experts can be combined to give a
system more breadth that a single person is likely to achieve
Reduce risk of doing business
– Consistency of decision making
– Documentation
– Achieve expertise
Entry barriers - expert systems can help a firm create entry barriers for potential
competitors
Differentiation - in some cases, an expert system can differentiate a product or
can be related to the focus of the firm (xcon)
Computer programs are best in those situations where there is a structure that is
noted as previously existing or can be elicited
ES Disadvantages:
 Limited knowledge
– “shallow” knowledge
» No “deep” understanding of the concepts and their relationships
– No “common-sense” knowledge
– No knowledge from possibly relevant related domains
– “closed world”
» The ES knows only what it has been explicitly “told”
» It doesn’t know what it doesn’t know
 Mechanical reasoning
– May not have or select the most appropriate method for a particular
problem
– Some “easy” problems are computationally very expensive
 Lack of trust
– Users may not want to leave critical decisions to machines
 How do you code common sense?
 Expertise is difficult to extract and encode.
 Expert “errors” transferred to model
 Another is that human experts adapt naturally but an ES must be recoded.
 Human experts better recognize when a problem is outside the knowledge
domain, but an ES may just keep working
 ES’s can’t eliminate the cognitive limitations of the user
 An ES is functional only in a narrow domain
Eng. Nareeman Sabry | Eng. Amal Ibrahim
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