The document discusses expert systems, which are computer programs that use knowledge and reasoning to solve complex problems like human experts. It describes key components of expert systems like the knowledge base, reasoning engine, and user interface. Examples of early medical expert systems MYCIN and Internist are provided that demonstrated how expert systems can model human diagnostic reasoning strategies. While expert systems have been effective in some domains, full integration into fields like medicine has proven challenging.

1. Expert Systems
Directors : Prof. Zixing Cai
&Miss WenSha
Central South University
College of Information Science
and Engineering

2. What is an Expert System?
Experts are people who are very familiar
with solving specific types of problems.
Expert System
Until now, no unified definition has been
given.
Knowledge-based system
The fundamental function of the expert
system depends upon its knowledge,
therefore, the expert system is sometimes C
called knowledge-based system. C IS I
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3. What is an Expert System(ES)?
Definition 1: ES can handle real-world complex
problems which need an expert’s interpretation
andIn short, an by using a computer model of
solve problems ES is an intelligent
human expert reasoning to reach the same
computer program that can
conclusions that the human expert would do if he
perform special and difficult task(s)
or she faces with a comparable problem.
Definition 2: ES is an intelligent computer program
in some field(s) at the level of to
that uses knowledge and inference procedures
human experts.
solve problems that are difficult enough to require
significant human expertise for their solutions.
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4. Architecture of ideal expert system
User Communication Knowledge
Interface Base
Interpreter
Plan Planner
Agenda Coordinator
Solution Adjuster
Reasoning
Blackboard Machine
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Architecture of an ideal expert system C
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5. ES-Knowledge Base(1)
Knowledge Base
To store knowledge from the experts of
special field(s). It contains facts and feasible
operators or rules for heuristic planning and
problem solving.
The other data is stored in a separate
database called global database, or
database simply.
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6. ES-Reasoning Machine(2)
Reasoning Machine
To memorize the reasoning rules and the
control strategies applied.
According to the information from the
knowledge base, the reasoning machine can
coordinate the whole system in a logical
manner, draw inference and make a
decision.
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7. ES- User Interface (3)
User Interface
To communicate between the user and
the expert system.
The user interacts with the expert system
in problem-oriented language such as in
restricted English, graphics or a structure
editor. The interface mediates information
exchanges between the expert system and
the human user. C IS IC
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8. ES- Interpreter(4)
Interpreter
Through the user interface, interpreter
explains user questions, commands and
other information generated by the expert
system, including answers to questions,
explanations and justifications for its
behavior, and requests for data.
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9. ES-Blackboard (5)
Blackboard
To record intermediate hypotheses and
decisions that the expert system
manipulates.
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10. ES-Note
Note:
Almost no exiting expert system contains
all the components shown above, but some
components, especially the knowledge base
and reasoning machine, occur in almost all
expert systems.
Many ESs use global database in place of
the blackboard. The global database
contains information related to specific tasks
and the current state. C IS IC
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11. Building Expert System
The key for successfully building an expert
system is to begin it from a smaller one, and
extend and test it step by step, make it into
a larger-scale and more perfect system.
The general procedure for building ESs :
Design of initial Knowledge Base
Development & test for prototype 原型
system
Improvement & induction 归纳 for the C IS IC
knowledgeUniversity Artificial Intelligence
Central South

12. Design of initial Knowledge Base
Problem identification
Knowledge conceptualization
Concept formulization
Rule formulation
Rule validation
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13. Stages for Designing KB
define key concept of the Re-designment
knowledge ,for example :
identify what the problem use knowledge change the knowledge to
check the
type of data structure ,
is , how to define it , can representation programming language of
correctness
conditions that have known,
we divide it into some sub Refinements
Questions Knowledge Concepts method to represent can be identified by
the goal state, assumption that rules or
problems the Structure
knowledge. the computer.
knowledge
and control strategy. Rules
Indentifi- Conceptu- Formali- Rule
Validation
cation alization zation Formalization
Concepts Conclusion
Representation
Stages for designing knowledge base C IS IC
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14. Types of Expert System (ES)
Category Problem Addressed
Interpretation Inferring situation descriptions from sensor data
Prediction Inferring likely consequences of given situation
Diagnosis Inferring system malfunction from observation
Design Configuring objects under constrains
Planning Designing actions
Monitoring Comparing observation to plan vulnerabilities
Debugging Prescribing remedies for malfunction
Repair Executing a plan to administer a prescribed remedy
Instruction Diagnosing, debugging and repairing student
behavior
Control C IS IC
Interpreting, predicting, repairing and monitoring
system behavior
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15. Expert Control Systems
Important differences between expert systems and
expert control systems:
Expert systems simply complete consultative
function for problems of special domains and aid
users to work.
Expert control systems need to make decisions to
control action independently and automatically.
Expert systems usually work in off-line mode.
Expert control systems need to acquire dynamic
information in on-line mode and make real-timeC
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control for the system.
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16. Two main types of expert control
Two main types of expert control:
 Expert control system
With a more complex structure, higher cost, better
performance, and used to plants or processes where
higher technical requirements are needed.
 Expert controller
With a simpler structure, lower cost and has a
performance that can meet the general
requirements for the industrial process control.
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17. Structures of Expert Control System
Operator Interface
Controller
Reasoning Machine Domain
Date
Control Knowledge Base Digital
Algorithms Processing
D/A A/D
Actuators Process Sensors
C
A typical structure of expert control system IS IC
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18. Tasks of Expert Control System
The expert control system should execute following
tasks:
 Supervise the operation of the plant (process) and
controller.
 Examine possible failure or fault of the system
components, replace these faulty components or
revise control algorithms to keep the necessary
performance of the system.
 In special cases, select suitable control algorithm to
adapt the variation of the system parameters and
environment. S IC CI
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19. Store the domain
knowledge of industrial
process control,experience
Extract and process of experts(expertise) and
Expert Controller Use the forward chaining Sum up every control
facts
information, provide
reasoning to judge the
Knowledge Base (KB) pattern and control
control strategy and learn
conditions of every rule in experience of the
adaptation with foundation
the sequence controlled process
K G
Feature
e Recognition S Inference I Set of U Y
Engine (IE) Control Rules Plant
R Information
- Processing
u
Sensor(s)
Industrial expert controller C IS IC
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20. Expert system-MYCIN
An early expert system developed in early
1970s at Stanford University
Wrote by Lisp Language
Author: Bruce G. Buchanan & Edward H.
Shortliffe
<<Rule-Based Expert Systems:
The MYCIN Experiments of the Stanford
Heuristic Programming Project >>
This expert system was designed to identify
bacteria causing severe infections C IS IC
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21. C IS IC
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22. Reasoning & Problem solving strategy
MYCIN could use backward chaining to find out
whether a possible bacteria was to blame.
“Certainty factor” is used for an assessment of the
likelihood 可能性评估 of one bacteria.
MYCIN’s problem solving strategy was simple:
For each possible bacteria: Using backward
chaining, try to prove that it is the case, finding
the certainty.
Find a treatment which ” covers” all the bacteria
above some level of certainty. IC
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23. MYCIN: Problem Solving
When trying to prove a goal through backward
chaining, system could ask user certain questions.
Certain facts are marked as “askable”, so if they
couldn’t be proved, ask the user.
The ask procedure is carried out in following style
of dialogue:
MYCIN: Has the patient had neurosurgery?
USER: No.
MYCIN: IS the patient a burn patient?
USER: No.
…
MYCIN: It could be Diplococcus.. C IS IC
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24. Modeling Human Diagnostic Strategies
Problem Solving Strategy used in MYCIN
only works when small number of
hypotheses (e.g., bacteria).
For hundreds of possible diseases, need a
better strategy.
Later medical diagnostic systems used an
approach based on human expert
reasoning.
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25. Diagnostic Reasoning: Internist
Internist is a medical expert system for
general disease diagnosis.
Knowledge in system consists of disease
profiles 概况 , giving symptoms 症状 associated
with disease and strength of association.
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26. Problem Solving in Internist
Use initial data (symptoms) to suggest, or trigger 引
发 possible diseases.
Determine what other symptoms would be
expected to confirm these diseases.
Gather more data to differentiate 区分 between
these hypotheses. Either:
If one hypothesis most likely, try to confirm it.
If many possible hypotheses, try to throw some out.
If a few hypotheses, try to discriminate 区别 between
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them.
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27. Medical Expert Systems Today
Medical expert systems were quite effective in
evaluations comparing their performance with
human experts.
Support the physicians 医生 decisions, rather than
doing the whole diagnosis.
Include many useful support materials 辅助材料 , such
as report generating tools, reference material etc.
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28. Summary: Expert Systems
Effective systems have been developed that
capture expert knowledge in areas like medicine.
Typically combine rule-based approaches, with
additional certainty/probabalistic reasoning, and
some top level control of the problem solving
process.
Not a huge take-up of systems, perhaps due to
failure to adequately consider how they would be
integrated into current practice.
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