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Introduction to Expert Systems

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Title: Introduction to Expert Systems


1
Introduction toExpert Systems
2
Expert Systems
  • Expert System
  • typically refers as an application of AI
    techniques in a business environment
  • its main objective is to capture experts
    experience and transfer it to non-expert users
    via the use of a computer system

(p3)

3
Question
  • Is ES really an expert???

(p4)
4
Answer
  • it is an expert to a certain degree only
  • Reasons
  • it depends on the accuracy and availability of
    knowledge that being collected

(p5)
5
ES benefits
  • capture of scarce expertise
  • reduce decision-making time
  • improve decision processes
  • reduced downtime (in Mfg)
  • easier for equipment operation
  • elimination the need of expensive equipment
  • Operation in hazardous environment (such as toxic
    environment)
  • Improvement on the process and product quality

(p6)
6
How (and where) ES have been applied?
  • 1. Concept of categorization

(p7)
(p8)
7
The Concept of Categorization
(p6)
8
Question
  • Do Expert Systems always work in real life?

(p9)
9
Answer
  • It depends on
  • accuracy of knowledge collected
  • availability of knowledge
  • working within a narrow domain of problem
  • evaluating criteria
  • limitation in according to
  • a) related issues
  • b) ES task categories

(p10)
(p11)
(p12)
(p13)
10
Criteria for Evaluation of an Expert System
  • The system should be responsive and easy to use.
  • The design and functionality should conform with
    current diagnostic standards.
  • The system should be able to function with
    incomplete knowledge.
  • The user should always control the consulting
    process.
  • The explanatory facility should be clear and
    user-friendly.
  • The knowledge base should contain generally
    accepted knowledge.
  • The system should be independent of any
    geographical constraints., that is mobility is an
    important success element
  • The system should allow for integration with all
    other current information systems.

(to p9)
11
Limitations Associated with Expert Systems
  • The needed knowledge is not always available.
  • Experts use common sense. Programming common
    sense is not yet a reality.
  • Expertise is difficult to extract and encode.
  • Experts can recognize a problem outside the
    knowledge domain much faster than an ES.
  • Expert systems cannot eliminate the cognitive
    limitations of the user.
  • An ES is functional only within a narrow
    knowledge domain.
  • Expert vocabulary is often limited and not easily
    understood by others.
  • Human experts adapt to their environments
    naturally while an ES must be explicitly updated.
  • An ES has a limited sensory experience compared
    to human experts.

(to p9)
12
Limitations Associated with Common ES Task
Categories
  • Task Problems
  • Interpretation Data may be noisy or missing
  • Data may be inaccurate
  • Prediction Must allow for contingencies and
    uncertainties
  • Diagnosis Multiple symptoms can confound
    diagnosis
  • Planning Multiple alternatives with complex
    scenarios
  • Design Conflicting constraints and
    interaction among sub-designs
  • Prescription Multiple problems may exist
  • Monitoring Error conditions and nominal
    expectations are often context- specifi
    c
  • Control Often requires interpretation based
    on common sense

(to p9)
13
Structure of EX
  • Main components of ES
  • 1. User
  • 2. User Interface
  • 3. Inference engine
  • 4. Knowledge base/acquisition systems
  • 5. Explanation systems
  • Their relationships

(p14)
(p15)
(p16)
(p18)
(p19)
(p20)
(p21)
14
1. User
  • refers to persons who interface with an ES
  • these ppl could be technical or non-technical
    staff however, they should only seek ES for
    consult of qualitative instead of quantitative
    problems

(p13)
15
2. User Interface
  • contains a communication system such as natural
    language processor , and some features described
    in DSS .. Menus, graphics ...
  • The use of multimedia devices

(p13)
16
3. Inference Engine
  • it is the main brain of ES
  • i.e. it governs on how knowledge should be
    retrieved
  • it also provides ways in which explanation,
    agenda and knowledge should be organized before
    presenting to end-users

(p17)
17
3. Inference Engine (cont)
  • Three major elements in Inference Engine
  • 1. Interpreter
  • (executes the given chosen agenda)
  • 2. Scheduler
  • (guides how knowledge should be searched)
  • 3. Consistency enforcer
  • (maintains and compiles sensitive solution set)

(p13)
18
4. Knowledge base/Acquisition system
  • involves ways in which knowledge should be
    represented in the system . (next lecture)
  • includes on how knowledge could be acquired
    ranging from human, texts, documents, private and
    public databases, reports, and WWW
  • includes on how knowledge should be searched

(p13)
19
5. Explanation System
  • ES provides crucial explanations to users
    relating to
  • a. how solutions are obtained/generated
  • b. why questions/information are asked by the
    system etc.

(p13)
20
Explanation Systems
Knowledge Base systems
Inference Engine
User Interface (such as a PC platform)
Users
(to p13)
21
Additional components
  • Blackboard (Workplace)
  • Knowledge refining system

(p22)
(p23)
(p24)
22
Blackboard (Workplace)
  • It is a workplace place to
  • a) work on a plan to attack problems
  • b) outline agenda for next execution steps
  • c) document solutions and alternative courses

(p21)
23
Knowledge refining system
  • it refers to ES learns and refines on its own
    knowledge
  • this component is not generally available in most
    commercial ES mainly most ES are rather small in
    scale

(p21)
24
Types of ES
  • 1. Rule-based
  • 2. Frame-based
  • 3. Semantic-based
  • 4. Case-based
  • 5. Model-based
  • 6. Hybrid Systems
  • 7. Ready-made systems (application packages)

25
Candidate Situations for ES Opportunities
  • Need for diagnosis of a problem situation or
    variance (audit, troubleshooting, etc.)
  • Need to understand the nature of a given
    situation
  • Need to predict the outcome of a current or
    future event
  • Need to control or govern a particular activity
    or process
  • Need to prescribe a solution or course of action
  • Need to evaluate and assess a prior event or
    process
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