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 called knowledge-based system.
We often talk of expert. But, what is the expert. Here, we give its definition:(念PPT).
And, what is Expert System? Until now, no unified definition has been given. I’ll give several kinds of definitions later on.
Then, what is Knowledge-based system? Because…(念PPT)

3. What is an Expert System(ES)?
In short, an ES is an intelligent computer program that can perform special and difficult task(s) in some field(s) at the level of human experts.
Definition 1: ES can handle real-world complex problems which need an expert’s interpretation and solve problems by using a computer model of human expert reasoning to reach the same conclusions that the human expert would do if he or she faces with a comparable problem.
Definition 2: ES is 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.
Here just are several kinds of definitions of Expert system.
(念PPT)

4. Architecture of ideal expert system
User
Communication Interface
Knowledge
Base
Interpreter
Blackboard
Reasoning
Machine
Plan
Planner
Agenda
Coordinator
This is the Architecture of an ideal expert system. We can see: it is composed of 5 parts: Knowledge Base; Reasoning Machine; Communication Interface; Interpreter; and Blackboard. And what is their usage? First, …(念ppt); Sencond, …(念PPT); ……
(最后) However, we must take note here:(点图下方标注, 念PPT)
Solution
Adjuster
Architecture of an ideal expert system

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.
(最后)The Blackboard, we mentioned just now, is really a global database.

6. ES-Reasoning Machine(2)
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.

7. To communicate between the user and the expert system.
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.
mediate [midieit] 调节

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.

9. ES-Blackboard (5)
Blackboard
To record intermediate hypotheses and decisions that the expert system manipulates.

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.

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 knowledge
Now, we’ll study how to build a 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.
(念PPT)

12. Design of initial Knowledge Base
Problem identification
Knowledge conceptualization
Concept formulization
Rule formulation
Rule validation
Design of the initial knowledge base is the most important and most difficult task. The design involves the following 5 stages: Problem identification(问题知识化); Knowledge conceptualization(知识概念化); Concept formulization(概念形式化); Rule formulation(形式规则化); Rule validation(规则合法化).

13. Stages for Designing KB
Re-designment
define key concept of the knowledge ,for example : type of data structure , conditions that have known, the goal state, assumption and control strategy.
identify what the problem is , how to define it , can we divide it into some sub problems
use knowledge representation method to represent the knowledge.
change the knowledge to programming language that can be identified by the computer.
check the correctness of rules or knowledge
Refinements
Questions
Knowledge
Concepts
Structure
Rules
Indentifi-
cation
Conceptu-
alization
Formali-
zation
Rule
Formalization
Validation
Concepts
Conclusion
Representation
The Stages for designing initial knowledge base can be shown in this figure.
(对着PPT讲)
Stages for designing knowledge base

14. Types of Expert System (ES)
Category
Problem Addressed
Interpretation
Prediction
Diagnosis
Design
Planning
Monitoring
Debugging
Repair
Instruction
Control
Inferring situation descriptions from sensor data
Inferring likely consequences of given situation
Inferring system malfunction from observation
Configuring objects under constrains
Designing actions
Comparing observation to plan vulnerabilities
Prescribing remedies for malfunction
Executing a plan to administer a prescribed remedy
Diagnosing, debugging and repairing student behavior
Interpreting, predicting, repairing and monitoring system behavior
Here are some types of Expert System. This is their Category(指着左边), This is the problems they can address.
This page, I won’t introduce in detail here, left you to read by yourself after class. OK?

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-time control for the system.
Just now, we introduced the Expert System. Now, we will learn the Expert Control System. Before this, we must have a look at the differences between expert systems and expert control systems.
(念PPT)

16. 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.
There are 2 main types of expert control: The Expert control system and Expert controller.
(念PPT)

17. Structures of Expert Control System
This is a typical structure of Expert Control System. Here, just the Controller.
This system should execute following 3 tasks:(快翻下一页)
A typical structure of expert control system

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.
(对着PPT念)

19. Feature Recognition Information Processing
Store the domain knowledge of industrial process control,experience of experts(expertise) and facts
Extract and process information, provide control strategy and learn adaptation with foundation
Use the forward chaining reasoning to judge the conditions of every rule in the sequence
Sum up every control pattern and control experience of the controlled process
Expert Controller
Knowledge Base (KB) K G
Feature Recognition Information Processing
Inference
Engine (IE)
Set of
Control Rules S I U
Plant Y e R - u
Now, we’ll discuss a specific structure of expert controller, that’s Industrial expert controller.
(对着书念)
(最后,翻开书讲解)
Sensor(s)
Industrial expert controller

20. An early expert system developed in early 1970s at Stanford University
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
Now, we’ll get a knowledge of a very famous Expert system-MYCIN.
(念PPT)

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.
治疗疾病：Cure Disease

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? …
MYCIN: It could be Diplococcus..
Neuro-surgery [njuərəu ‘sə:dʒəri] 神经外科

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.
Let’s have an analysis about the defects and development trend of MYCIN. (念PPT)
Diagnostic [daiəg nɔstik] 诊断的

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.
Internist 内科医生

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 them.

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.

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.