Lecture 7
EXPERT CONTROL SYSTEMS
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.
Branches of Computational Intelligence
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.
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.
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”.
7.1 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.
7.1 Elements of an Expert System
Expert System Structure
Explanation
facility
Inference
engine
Knowledge
base
Knowledge
base
acquisition
facility
User
interface
Experts
User
Knowledge Base




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
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.
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
Forward Chaining



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
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
Backward Chaining


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
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
Knowledge Acquisition Facility

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
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.
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
7.2
Stages in the Development of an Expert
System
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
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Objectives ---Problem Definition
Knowledge Acquisition and Knowledge
Representation
Rapid Prototype
Implementation
Test and maintain
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.
Knowledge Acquisition

" the transfer and transformation of
potential problem-solving expertise from
some knowledge source to a program.”
- Buchanan 1983.
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"
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.
Knowledge Representation


A method to represent the knowledge about
the domain
major methods:

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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.
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.
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.
Participants in Expert Systems
Development and Use

Domain expert


Knowledge user


The individual or group whose expertise and knowledge is
captured for use in an expert system
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
Expert
system
Knowledge engineer
Domain expert
Knowledge user
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.
General Approaches to Building
Expert Systems

Build "in-house" using a shell

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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.
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).
Expert Systems Development
Alternatives
high
Develop
from
shell
Development
costs
low
Develop
from
scratch
Use
existing
package
low
high
Time to develop expert system
When to Use an Expert System (1)


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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
When to Use an Expert System (2)


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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
Limitations

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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.
Limitations

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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.
Categories of Expert Systems
Category
Problem Addressed
Interpretation
Inferring situation descriptions from observations
Prediction
Inferring likely consequences of given situations
Diagnosis
Inferring system malfunctions from observations
Design
Planning
Configuring objects under constraints
Developing plans to achieve goals
Monitoring
Comparing observations to plans, flagging exceptions
Debugging
Prescribing remedies for malfunctions
Repair
Instruction
Administer a prescribed remedy
Diagnosing, debugging, and correcting student
performance
Interpreting, predicting, repairing, and monitoring system
behavior
Control
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 knowledgebased structures, thus making the control systems more flexible and
adaptive than conventional control systems.
7.3 Concepts and Characteristics
of Expert Control Systems
comparison of conventional expert systems and expert control system
7.3 Concepts and Characteristics
of Expert Control Systems
comparison of expert control and traditional advanced control
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;
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.
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.
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.
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
7.4 Classification of Expert Control Systems
Rule-based
expert tuning or adaptive controllers
7.4 Classification of Expert Control Systems
Expert
supervisory control systems
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.
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
7.5 Design Principles of Expert Control Systems
7.5.1 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:
7.5 Design Principles of Expert Control Systems
7.5.2 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.
7.5 Design Principles of Expert Control Systems
7.5.3 Hierarchical structure of decision-making
Knowledge Refinement
Knowledge base & Inference Engine
Planning & Management
Supervisory control & Emergency Handing
Real-time Intelligent Control
7.5 Design Principles of Expert Control Systems
7.5.4 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.
7.5 Design Principles of Expert Control Systems
7.5.5 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.
7.5 Design Principles of Expert Control Systems
7.5.6 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.
7.6 Architecture of Expert Control Systems
Figure 7.8 A generic architecture of expert control system
7.6 Architecture of Expert Control Systems
Fig. 7.9 general basic structure of expert control
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.
7.7 Development Methods of Expert Control Systems
Figure 7.10 Schema diagram of ECS development
seven stages
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