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2. Lecture Expert Systems Soft Control (AT 3, RMA)
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2. Outline of the Lecture
1. Introduction to Soft Control: Definition, Limitations, Basics of
“SMART” Systems
2. Knowledge representation and knowledge processing
(Symbolic AI) Application: Expert Systems
3. Fuzzy-Systems: Dealing with Fuzzy Knowledge
Application: Fuzzy-Control
4. Connective Systems: Neural Networks
Usage: Identification and Neural Control
5. Genetic algorithms: Stochastic Optimization
Application: Optimization
6. Summary & Literature
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Contents of the 2nd Lecture
1. Expert Systems
1. Idea
2. Areas of applications
3. Compared with conventional programs
2. Basic Architecture of Expert Systems
Explanation of the components
Forms of knowledge base
Inference mechanism
3. Case Based Reasoning
4. Summary
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Expert Systems
• Core idea (Natural Model)
Human-like abstract thinking
• History
First expert systems began in 1970's (though faced the problem of high
computing expenses)
• Application in Automation Engineering
Today: Manifold industrial use at higher levels of automation
• Examples
Expert systems to support process control
Expert systems for fault diagnosis
Training Systems (simulators)
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Comparison: Conventional Programs vs. Expert Systems
In expert systems the knowledge & the problem-solving strategy
are separated, while in conventional programs knowledge and
problem-solving strategy are implicitly embedded in algorithms.
Algorithms
Data
Knowledge
Data
Troubleshooting
-strategy
Conventional Programs Expert Systems
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Architecture of Expert Systems
Quelle: Lunze
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Knowledge Acquisition Components
• The knowledge of the system is changed or reviewed by introducing Interface to the expert systems
• Ideally, the interface is designed such that no programming knowledge is required
• Very often, there is a system developer (knowledge engineer) between the knowledge acquisition component and the expert system
• The knowledge engineer supports the experts in the command input or he usually asks the expert’s knowledge in interviews and prepares the input according to it.
• Most importantly, knowledge acquisition creates a bottleneck in the expert system creation
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Knowledge Base
• Here the existing knowledge in the system is suitable saved
• There are three different components of the knowledge base
1. Array based knowledge: The actual knowledge base, is the knowledge, that is
entered by knowledge engineer or expert
2. Specific case knowledge: knowledge about the current system problem, which
is entered by the user or automatically,
3. Interim results: Results that have been produced by individual rules of the
problem-solving components can serve in other rules and further processing
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Problem Solving Components
• Application neutral (application independent) part of the knowledge
Processing (also inference machine)
• Program system, the means of knowledge basis generation for
problem-solving
• Construction of the inference machine depends on the type of
knowledge base
Chain-rule (forward, backward) for production control systems
Derivation (resolution) for logic based systems
Comparison with case-based reasoning system
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Dialog Components
• Interface for users of the expert system
• Ask the problem from suitable
• Presentation and explanation of the solutions
• Explanation of the problem
System can be transparent, as it was concluded. (Useful for troubleshooting and
necessary confidence-building)
Deep explanations for "why" and “for which reason" a solution is possible or not
possible.
The XPS approaches in the deep structure of problems; that are still in their
infancy ( deep structure = the problem underlying technical, physical or
chemical relationships)
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Forms the Knowledge Base
• Control Based Systems
Production Control Systems
Logic Oriented Systems
• Case Based Reasoning Systems
• Object-Oriented Systems (are not discussed here)
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Production Control Systems
• Production rules as the smallest building blocks of knowledge base
• Construction:
IF certain conditions are met,
THEN will be closed on the following facts
• Example: failure diagnosis (syntax of the system Babylon)
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The General Approach to the Inference
Quelle: Lunze
• MATCH: It examines
which rules are
applicable in the
current problem state
conflict quantity =
quantity in all
applicable rules
• SELECT: selecting a
rule from the conflict
quantity
• ACT: application of
the selected rule to
the current problem
state new problem
state
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Inference: Forward Chaining vs. Backward Chaining
• Forward Chaining:
Carried out on the basis of known facts
Assumptions for these are new facts
Interruption, if no rule can fire more
Undirected (Untargeted) search
• Backward Chaining
Carried out on a hypothesis that would find the rule for re-check
A rule (to be found); that is conclusion to hypothesis
Then, with the assumption of this rule; proceed to the already known rules
• Mixed Strategy
Initially Hypothesis formation, (forward) = selection of the rule that is valid for
most of the assumptions
Then Hypothesis check (backward) = attempt the missing assumptions to verify
• For all chaining methods search may still exist between depth and
the breadth
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Example: Forward Chaining
• Rules are given in the form
of a decision tree
• Problem: Find reason for
low pressure
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Logic Based Systems
• Based on the statements or first order logic
• Use of variables is allowed
• Application e.g. Logic oriented programming PROLOGUE
• Knowledge base is developed from facts (statements) and rules
(implications)
• Example:
Statements:
A1 = stirrer runs
A2 = inert atmosphere is concerned
A3 = footway is off
A4 = dosage runs
A5 = dosage does not work
Implications
(A1 & A2 & A3) A4
A3 A4
Instructions
Be A1, A2, A3 TRUE then the value of A4 will also be derived to TRUE
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Logic Based Systems: Extended
• Default logic: For unknown assumptions are default values are (generally) TRUE
• Multi-logics: Z.B. tetravalent (TRUE, probably TRUE, FALSE Probably, FALSE)
• Modal logic: "It is possible that ... applies "
• Auto-epistemic logic: "I believe that ... applies "
• Temporal logic: the light temporal relations: "A, applies after B“
• Inclusion of probability statements in the logic
• Fuzzy Logic: details in the section of fuzzy systems
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Problems Rule-Based Systems
• Basic assumption that technical expertise expressed in rules can be
at least questionable
• Rules are often encroached, control logic in the system to encode
• In rules the context (scope) is often encoded
• In rules structural relations are often encrypted, for example,
specializations of some rules that other rules
• Rules can not be structured and organized
Mixing of knowledge (facts, context, control) means that expert
systems are not able to create their own system behavior that is
sufficient to explain .
Lack of structure leads to the technical applicability limits (generally
sensible systems have thousands of rules)
Way out: Case based reasoning and object-oriented approaches
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Case Based Reasoning Systems
Case Based Reasoning, CBR
• Case is some kind of experience in solving a problem .
• Use of the experience, or a case, the solution for a sufficiently similar problem to new current problem can be applied.
• Special development of knowledge-based systems
• Cases from other analogous or same area (different domains: the solar system and atomic model) Case Retrieval (solution unchanged over)
Close Case (Case adapt)
• Renunciation of truth, instead usefulness (optimization problem)
• Approach is based on dynamic memory (basic assumptions) Remembering and adjust (adaptation) are key in understanding mental
processes
Indexation is important for remembering
Understanding leads to the reorganization of memory, which is why this dynamic
The memory structure for the knowledge processing are the same as for the storage of knowledge
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CBR: Application Requirements
1. There must be sufficiently many experiences available
2. It is must be easier to use this experience as the problems may be
solved directly
3. The use of the solutions on the case by case basis should not
conflict with safety requirements
4. The available information is incomplete or vague and uncertain
5. A modeling within the meanings of traditional knowledge systems is
not easily available
• Frequently used in the area of diagnostics and electronic sales but
also configuring and planning
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CBR: Example
• Appropriate selection of similarities among cases
• How does one define similarity
1. Definition of similar dimensions for the individual attributes (local similarity
degree)
2. Summary of an overall dimensions (e.g. weighted sum)
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Summary and Learning of the 2nd Lecture
To know what Experts Systems are
Application areas of Expert Systems
To know basic architecture of Expert Systems and its components
To know basic types of Expert Systems and their functional
principle:
Production Control Systems
Logic Based Systems
Case based systems
