Lecture 7 EXPERT CONTROL SYSTEMS

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Lecture 7EXPERT CONTROL SYSTEMS
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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.
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Branches of Computational Intelligence
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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.
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Definition of Expert System
7.1 Elements of an 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 experts model
  of the domain.

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Definition of Expert System
7.1 Elements of an 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.

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knowledge --two components
7.1 Elements of an Expert System

• 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.

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Expert System Structure
7.1 Elements of an Expert System
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Inferenceengine
Explanationfacility
Knowledgebaseacquisitionfacility
Userinterface
Knowledgebase
Experts
User
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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(???) a fashion(?????) as possible

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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.

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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

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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
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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

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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
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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

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Knowledge Acquisition Facility

• Knowledge acquisition facility
• Provides a convenient and efficient means of
  capturing and storing all components of the
  knowledge base

Knowledgebase
Knowledgeacquisitionfacility
Joe Expert
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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.

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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

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7.2 Stages in the Development of an Expert
System

• Objectives ---Problem Definition
• Knowledge Acquisition and Knowledge
  Representation
• Rapid Prototype
• Implementation
• Test and maintain

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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.

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Knowledge Acquisition

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

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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"

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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.

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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.

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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.

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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.

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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

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Expertsystem
Knowledge engineer
Domain expert
Knowledge user
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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.

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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.

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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).

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Expert Systems Development Alternatives
high
Developfromscratch
Developfromshell
Developmentcosts
Useexistingpackage
low
low
high
Time to develop expert system
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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

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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

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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.

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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.

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Categories of Expert Systems
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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.

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

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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

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7.4 Classification of Expert Control Systems

• Rule-based expert tuning or adaptive controllers

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7.4 Classification of Expert Control Systems

• Expert supervisory control systems

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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.

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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

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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

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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.

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7.5 Design Principles of Expert Control Systems
7.5.3 Hierarchical structure of decision-making
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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.

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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.

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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.

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7.6 Architecture of Expert Control Systems
Figure 7.8 A generic architecture of expert
control system
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7.6 Architecture of Expert Control Systems
Fig. 7.9 general basic structure of expert
control
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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.
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7.7 Development Methods of Expert Control
Systems
Figure 7.10 Schema diagram of ECS development
seven stages