1. Lecture 7 EXPERT CONTROL SYSTEMS

2. Artificial intelligence, in particular expert system techniques, have been developing rapidly in control engineering. Applications of expert-system techniques in control engineering control-system design, fault diagnosis, simulation, modeling and identification, on-line performance monitoring, adaptation and auto-tuning and supervisory control.

3. Branches of Computational Intelligence

4. 7.1 Elements of an Expert System
conventional computer software can be viewed as the synergy of:
In contrast, computer software used in Expert Systems can be described as the synergy of:
The most significant characteristic of this class of systems is that it draws on human knowledge and emulates human experts in the manner with which they arrive at decisions.

5. Definition of Expert System
7.1 Elements of an Expert System
Definition of Expert System
A computing system capable of representing and reasoning about some knowledge rich domain, which usually requires a human expert, with a view toward solving problems and/or giving advice. Such systems are capable of explaining their reasoning.
Does not have a psychological model of how the expert thinks, but a model of the expert’s model of the domain.

6. Definition of Expert System
7.1 Elements of an Expert System
Definition of Expert System
“An Expert System is the embodiment of knowledge elicited from human experts, suitably encoded so that the computa-tional system can offer intelligent advice and derive intelli-gent conclusions on the operation of a system”.

7. knowledge --two components:
7.1 Elements of an Expert System
knowledge --two components:
• facts, which constitute ephemeral information subject to changes with time (e.g., plant variables) and
• procedural knowledge, which refers to the manner in which experts in the specific field of application arrive at their decisions.

8. Expert System Structure
7.1 Elements of an Expert System
Expert System Structure

9. Knowledge base acquisition facility
Explanation facility
Inference engine
Knowledge base
Knowledge base acquisition facility
User interface
Experts
User

10. Knowledge Base knowledge specific to the domain
Stores all relevant information, data, rules, cases, and relationships used by the expert system
knowledge specific to the domain
facts specific to the problem being solved
Knowledge Representation is the key issue
Aim is usually to present the knowledge in as “declarative（陈述的）” a fashion（样式、方式） as possible

11. Inference Engine
Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions in the way a human expert would
Manipulates the knowledge base to solve the given problem
This is the "procedural knowledge", how to put the facts and domain knowledge together to reach a solution.

12. Basic ways inference engines work:
forward chaining (forward reasoning)
FACTS = X
IF X, THEN Y
add Y to the blackboard which contains the facts
start with the FACTS and work forward through the rules to find a solution
match FACTS to all possible RULES.
A method of reasoning that starts with the facts and works forward to the conclusions

13. Forward Chaining Goal Forward Chaining Initial Knowledge/Facts
In this process the knowledge base is searched for rules that match the known facts, and the action part of these rules is performed.The process continues until a goal is reached.
Puts the symptoms together to reach a conclusion
ex. Doctor diagnosing a patient
Goal
Forward Chaining
Initial Knowledge/Facts

14. Basic ways inference engines work
backward chaining (backward reasoning)
starts with the knowledge base - thinks of these as goals we are trying to obtain:
Y = result of rule (solution)
verify if FACTS (X) support the rule
start with possible solution, and search facts to see if rules can be supported
A method of reasoning that starts with conclusions and works backward to the supporting facts

15. Backward Chaining Goal Backward Chaining Initial Knowledge/Facts
Starts form a goal, the conclusion. All the rules that contain this conclusion are then checked to determine whether the conditions of these rules have been satisfied
Ex. Doctor has end idea of what is wrong with patient but know they must prove it by going from the diagnosis and finding symptoms
Goal
Backward Chaining
Initial Knowledge/Facts

16. Explanation Facility Explanation facility
A part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results

17. Knowledge Acquisition Facility
Provides a convenient and efficient means of capturing and storing all components of the knowledge base
Knowledge base
Knowledge acquisition facility
Joe Expert

18. User Interface
Expert systems are interactive; a session between the user and the KBS is necessary to generate a solution.
The interface is important since it provides the user with the ability to interact with the system.
A good user interface will increase users’ confidence in the system.
A poor interface will frustrate users and can cause a loss of confidence in the results of the system.

19. User Interface
The user interface also implements the explanation capability.
Essential is the ability to answer questions such as:
Why?
How?
What?
Frequently:
the ability to define terms

20. 7.2 Stages in the Development of an Expert System
Objectives ---Problem Definition
Knowledge Acquisition and Knowledge Representation
Rapid Prototype
Implementation
Test and maintain

21. objectives The essential problem is selecting an appropriate domain:
the problem must require some type of specialized knowledge, if there are human "experts" this criteria is probably satisfied
must not be overly large: define the problem fairly narrowly.
in business organizations, it should a problem that is handled often enough that an investment is expected to have some payoff: the once every 5 years sort of problem going to payoff.

22. Knowledge Acquisition
" the transfer and transformation of potential problem-solving expertise from some knowledge source to a program.”
- Buchanan 1983.

23. Knowledge Acquisition
machine learning - building capabilities into the system that allow it to learn from what it is doing.
the problem of induction - how many instances must be observed before it can be added to the knowledge base as "true"

24. Knowledge Acquisition (cont.)
knowledge elicitation - extract the knowledge from the human expert, through some means
direct - interaction with the human expert interviews, protocol analysis, direct observation, etc.
indirect - utilize statistical techniques to analyze of data and draw conclusions about the structure of the data.

25. Knowledge Representation
A method to represent the knowledge about the domain
major methods:
Decision tree
Programming language
logic
Although a shell contains a way to represent knowledge, shell selection should be influenced by the matching the representation to the knowledge in the domain.
Knowledge must be coordinated, so that the knowledge base is consistent.

26. Prototype
Typically use an "incremental" development approach to an expert system.
Build an initial prototype and adjust and expand
Allow the expert to interact with the prototype to get feedback
Reevaluate if the project should be continued, if major redesign (knowledge representation) is necessary, or to go ahead.

27. Test and maintain
New rules can be continually added and old ones refined/ removed.
This is a tricky process, but there does not seem to be much literature on it.
One characteristic of an Expert system should be maintainability, so the ability to add/change/delete rules is essential.

28. Participants in Expert Systems Development and Use
Domain expert
The individual or group whose expertise and knowledge is captured for use in an expert system
Knowledge user
The individual or group who uses and benefits from the expert system
Knowledge engineer
Someone trained or experienced in the design, development, implementation, and maintenance of an expert system

29. Expert system
Knowledge engineer
Domain expert
Knowledge user

30. General Approaches to Building Expert Systems
Purchase a developed system
Not that many exist, as packages are common for certain applications that are common to many businesses.
See expertise embedded in some applications, e.g., Turbo-Tax, network diagnostics.

31. General Approaches to Building Expert Systems
Build "in-house" using a shell
A shell provides an inference engine, a user interface, and a way to represent knowledge.
Develop the knowledge base for the particular problem domain.
The focus of development is on knowledge acquisition.
Many shells are available for purchase.

32. General Approaches to Building Expert Systems
Build from scratch using an AI language
Requires specialized training to effectively program in these languages.
Few people are trained in these approaches, and these approaches are time consuming and expensive (shells are typically a much more economical approach).

33. Expert Systems Development Alternatives
high
Develop from scratch
Develop from shell
Development costs
Use existing package
low
low
high
Time to develop expert system

34. When to Use an Expert System (1)
Provide a high potential payoff or significantly reduced downside risk
Capture and preserve irreplaceable human expertise
Provide expertise needed at a number of locations at the same time or in a hostile environment that is dangerous to human health

35. When to Use an Expert System (2)
Provide expertise that is expensive or rare
Develop a solution faster than human experts can
Provide expertise needed for training and development to share the wisdom of human experts with a large number of people

36. Limitations
Lack common sense: A KBS handles problems in a very narrow range.
Difficult to capture “deep knowledge” of a problem domain.
MYCIN, which diagnosis bacterial blood diseases, does not know what blood does or the function of spinal cord. One story is that MYCIN asked if a patient was pregnant after being told the patient was a man.
Inability to provide deep explanation, i.e., why it applied certain rules.

37. Limitations
Lack robustness: expertise is brittle. When a human expert cannot solve a problem readily, they use their deep knowledge to come up with a strategy to attack a problem.
Difficult to verify. An important consideration as KBS approaches are applied to critical applications.
Little learning from experience. There are some inferential techniques, but they have their own limitations.

38. Categories of Expert Systems
See copy of p. 93 Turban

39. 7.3 Concepts and Characteristics of Expert Control Systems
Definition
Expert control (or knowledge-based control) refers to methods that utilize expert-system techniques and control theory to design control systems that can auto-mate some of the tasks currently performed by human experts, and which cannot be carried out by traditional control systems
key point
EC is the incorporation of heuristics and logic through knowledge-based structures, thus making the control systems more flexible and adaptive than conventional control systems.

40. 7.3 Concepts and Characteristics of Expert Control Systems
comparison of conventional expert systems and expert control system

41. 7.3 Concepts and Characteristics of Expert Control Systems
comparison of expert control and traditional advanced control

42. The fundamental functions of ECSs
(1) Take over the skilled operators' routine tasks and give effective controls for processes which are time-varying, non-linear, and subjective to various disturbances.
(2) Take advantage of all the available prior knowledge and on-line information;
(3) perform fault diagnosis on the control system operation and components, including the detection of actuator and sensor problems;
(4) operate reliably and conveniently;
(5) Increase the amount of process knowledge, and accordingly improve the control system's performance;

43. The fundamental functions of ECSs
(6) represent control knowledge in an effective way which easily allows for modification and extension;
(7) Maintain dialogue with the user and give explanation of reasoning results, and also obtain information from the user;
(8) require a minimal amount of prior knowledge;
(9) Have a capability for real-time reasoning and decision making.

44. suitable application areas for ECSs
(1) ill-structured processes for which mathematical models do not exist or are inadequate;
(2) Complex problems which require answers within a limited time interval, such as fault diagnosis and emergency handling;
(3) Situations where expertise is required for problem-solving but where there are not enough experts for the task;
(4) Situations where qualitative or uncertain information must be processed, and symbolic logic is required for problem-solving;
(5) complicated problems where a heavy computing burden and high cost would be involved when using conventional algorithmic methods;
(6) Cases where operating conditions change frequently and/or severely.

45. 7.3 Concepts and Characteristics of Expert Control Systems
Definition
Expert control (or knowledge-based control) is one of the intelligent control methods, which combines control theory and expert-system techniques to design and realize in the autonomous operation of complex, uncertain or ill-defined physical processes.
An ECS is an intelligent control system which uses expert-system techniques on difficult control problems where analytic models do not exist or are inadequate, and require expert knowledge for their problem-solving.

46. 7.4 Classification of Expert Control Systems
Rule-based expert tuning or adaptive controllers
Expert supervisory control systems
Hybrid expert control systems
Real-time control expert system

47. 7.4 Classification of Expert Control Systems
Rule-based expert tuning or adaptive controllers

48. 7.4 Classification of Expert Control Systems
Expert supervisory control systems

49. 7.4 Classification of Expert Control Systems
Hybrid expert control systems
a composite intelligent control system which utilizes a multilayer hierarchical structure and the incorporation of various techniques, including expert systems, pattern recognition, fuzzy logic, neural networks, and computer process control.

50. 7.4 Classification of Expert Control Systems
Real-time control expert system
a typical real-time expert system with all the characteristics of an expert system, such as modularity (flexibility), heuristics and transparency, as well as the features of a control system, e.g. real-time operation, reliability, and adaptation, etc

51. 7.5 Design Principles of Expert Control Systems
Modeling with multiple representation forms
knowledge representation in ECS can be grouped into two parts:
system modeling (including the controlled process and controllers), and
maintaining the relevant information and knowledge essential to perform the intelligent control and supervision tasks.
Multiple representation forms should be used in modeling mainly because:

52. 7.5 Design Principles of Expert Control Systems
Eliciting and recognizing characteristic information
One of the important features of intelligent control is to classify and extract on-line information in an effective way. In a complex system, a large number of sensor data and noisy signals could enter the system continuously. It is very important to collect, catalogue and dispense the information in an organized way. Therefore, the emphasis of information processing is on eliciting and recognizing characteristic information that can reflect the system properties, and converting them into the knowledge the decision-making requires.

53. 7.5 Design Principles of Expert Control Systems
Hierarchical structure of decision-making
Knowledge Refinement
Knowledge base & Inference Engine
Planning & Management
Supervisory control & Emergency Handing
Real-time Intelligent Control

54. 7.5 Design Principles of Expert Control Systems
Real-time inference with multiple strategies
In ECSs, the inference engine should provide the Mechanism that evaluates, interprets, and executes the data and knowledge to generate inferences or sequences of actions to be executed under time constraints.
ECSs need to reason about a number of past, present and future events.
ECSs must be capable of being interrupted, to accept inputs from unscheduled or asynchronous events, reasoning by a variety of means and techniques.
Usually, different inference strategies should be used in different decision levels or different tasks.

55. 7.5 Design Principles of Expert Control Systems
Introducing intelligent control into the real-time level
concentrate only on the intelligence in the higher levels, such as supervision, learning or adaptation, planning, etc., and adopt traditional control techniques such as PID algorithms at their real-time level.

56. 7.5 Design Principles of Expert Control Systems
On-line stability monitoring
ECS is essentially non-linear, time-dependent, and also unstructured. Thus, it is very difficult to analyze the stability of an ECS by mathematical methods." Therefore, on-line monitoring of the system behavior (e.g. acceptable behavior, malfunction behavior and fault behavior,") and prediction of the possible states to keep the system behavior within an acceptable area, is an effective way to achieve guaranteed system stability.

57. 7.6 Architecture of Expert Control Systems
Figure 7.8 A generic architecture of expert control system

58. 7.6 Architecture of Expert Control Systems
Fig general basic structure of expert control

59. 7.7 Development Methods of Expert Control Systems
The main tasks of developing an ECS can be grouped into three parts:
(1) Build the models of the process; including problem definition, model selection, knowledge acquisition, etc.
(2) Construct an expert controller; involving building the knowledge base and inference engine, constructing the system structure, determining knowledge representation paradigms, selecting the control strategies and parameters, etc.
(3) Establish a user-friendly interface; consisting of human-computer interface design and management.

60. 7.7 Development Methods of Expert Control Systems
Figure Schema diagram of ECS development
seven stages