introduction to expert systems (1 of 2)
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INTRODUCTION TOEXPERT SYSTEMS
Lecture-1/2
By
Dr. M. Tahir Khaleeq
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Expert Systems
Expert Systems are knowledge-based systems whichcontain expert knowledge and can provide an expertise,
similar to the one provided by an expert in a restricted
application area.
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An expert system is a program that can provide expertise
for solving problems in a defined application area in the
way the expert do.
The two sides of an expert system:
1. The experts side
2. The users side
It takes a long time (usually several years) to become an
expert.
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Example:
Expert system for diagnosis of cars has a knowledge base
containing rules for checking a car and finding faulty
elements, as it would be done by a specialized engineer.
IF (meter is OK)
AND [TEMPERATURE] > 120
THEN Cooling system is in the state of overheating
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Expert Systems? Why
Order-of-magnitude increase by expert in solving tasks
Increased quality of work
Reduced errors Reduced cost
Decreased personnel and training time
Improved decisions
Improved customer service
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Expertise
Expertise is the extensive, task-specific knowledgeacquired from training, reading, and experience.
Expertise includes:
- Facts about the problem area.- Theories about the problem area.
- Hard-and-fast rules and procedures regarding the
general problem area.
- Rules(heuristics) of what to do in a given problemsituation (rules regarding problem solving).
- Global strategies for solving these type of problems.
- Meta - knowledge (knowledge about knowledge)
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Facts about Expertise
Experitse is usually associated with
High degree of intelligence Quality of knowledge
Experts
Experts behave the following activities: Recognizing and formulating the problem
Solving the problem quickly and properly
Explain the solution
Learning from experience (past successes and mistakesRestructuring knowledge
Determining relevance
Awareness of limitations.
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Expert Systems Facilities
Representing existing expert knowledge
Accommodating existing database
Learning and accumulating knowledge during
operation Learning new pieces of knowledge from existing
database
Making logical inferences Making decision and giving recommendations
Communicating with user in a friendly way.
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Expert systems have been used successfully in about every
field of human activity, including Engineering, Science,
Medicine, Manufacturing, Education and Training,
Business, Finance and design.
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Expert System Shells
Previous Expert systems were all written in Lisp, C,Prolog, etc...
In order to speedup the design process, expert system
shells are developed. They have architecture of an expert
system, but are empty of knowledge. The knowledge isentered in the empty knowledge-based modules of the
shell.
By using shells it becomes much easier to build an
Expert System.
Examples: EMYCIN, OPS5 (rule-based), SOAR
(revision of OPS5 with chunking), KADS (Knowledge
Acquisition), CSRL, DSPL, RA, Peirce (GT tools)
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Expert System Architecture
Knowledge
Base
Data
Base
Inference
Engine
Knowledge
Acquisition
User
InterfaceExplanation
Experts
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The Knowledge Base Module
It is where the problem knowledge residesIt may be a production memory in production
languages.
It may be a set of fuzzy rules in a fuzzy system
It may be neural networks (has been trainind withthe past data) in the connectionist expert systems.
The Database Module
It is the working memory in the production languages. In some architectures this module can be used as a source
of knowledge (past data) in addition to the knowledge
base.
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The Inference Engine
It is a program that controls the functioning of the expert
system. It contains an inference mechanisms, either forward
chaining, or backward chaining, or a combination of
them.
The Explanation Module
It is a module that traces the execution of the expert
system and transfers this accumulated information to theuser.
EX: WHY it is checking a condition element in a rule, or
HOW it has inferred some conclusion.
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Stop the car
There is over heating
The brakes respond slowly
The meter works properly The temperature is over 120
How?
Why?
How?Why?
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The User Interface Module
Its role is to communicate with the environment, tointeract with the user in a friendly way.
Natural language and speech processing may be used
for communication with users.
The Knowledge Acquisition Module
It is designed to accumulate knowledge to build up the
knowledge base.
Knowledge Acquisition: Interview experts Learning from data Literature
Agents on the Web
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An expert systems design is very much a heuristic processalthough some global phases have been shared among
expert system designers.
Five Stages of Expert System Development
IDENTIFICATION CONCEPTUALIZATION FORMULATION
REALIZATIONVALIDATION
Expert System Design
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Stage-1 Identification of the Problem
What class of problems with the expert system be
expected to solve?
How can be these problem be defined?
Stage-2 Conceptualization
What is given and what should be inferred?
What types of data and knowledge are available?
Is there a need for knowledge acquisition?
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Stage-3 Formulization
Here major issues related to the knowledge should be
discussed. The issues are:1. Representation 2. Inference 3. Learning
4. Generalization 5. Interaction 6. Explanation
7. Validation 8. Adaptation.
Are data and knowledge are insufficient, or plentifuland redundant?
Is there a need to deal with uncertainty?
Are data and knowledge reliable, accurate and precise,
or unreliable, inaccurate, and imprecise?Are they consistent and complete?
What kind of explanation is needed/
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Stage-4 Realization
What methods and tools are appropriate for
representation data and knowledge: data basedsystems, symbolic AI methods, fuzzy systems, neural
networks etc?
Extendibility, friendliness, reliability, robustness of
the realization.Stage-5 Experiments and Validation of the Results
How to evaluate the system and the error?
How to validate the results:
Compare experimental results with the resultsobtained by experts,
Compare results with the results obtained by other
methods etc.
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The Iterative Process of Identifying the Problem
Does
Description Adequately
Describe
Problem?
Expert Describes CasesKnowledge Engineer Writes
Problem Description
Description Complete
NO
YES
Process
begins
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The Iterative relationship between
the Identification and Conceptualization
Is
Identification
Adequate ?
Identification Conceptualization
Development Process Continues
NO
YES
Process
begins
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1. How to acquire knowledge from experts?
2. How to extract knowledge from a huge mass of
previously collected data?
3. How to represent incomplete, corrupted and
contradictory data and knowledge?
4. How to perform approximate reasoning?
Fuzzy systems and neural networks provide solutions to
these problems.
Main Problems in Building
Expert Systems
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Categories of Expert Systems
Interpretation SystemsInferring situation descriptions from observations.
Ex: Surveillance, Speech understanding, Image analysis,
Signal interpretation, and many kinds of intelligence
analysis.
Prediction Systems
Inferring likely sequences of given situations.
Ex: Weather forcasting, Demographic predictions,Economic forcasting, Traffic predictions, Crops
estimates, and Military, Marketing, or Financial
forecasting.
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Diagnostic Systems
Inferring system malfunctions from observations.
Ex: Medical, Electronic, Mechanical, Software.
Design Systems
Configuring objects under constraints.
Ex: Circuit layout, Building design, Plant layout.
Planning Systems
Develop plans to achieve goals.
Ex: Project management, Routing, Communications,Product development, Military applications and
Financial planning.
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Monitoring Systems
Compare observations of system behavior with standards.
Ex: Plant monitoring, Aircraft monitoring.Debugging Systems
Prescribing remedies for malfunctions.
Repair Systems
Develop and execute plans to administer a remedy forsome diagnosed problems. Such systems incorporate
debugging, planning and execution capabilities.
Instruction Systems
Diagnosing, debugging, and correcting studentperformance.
Control Systems
Adaptively govern the overall behavior of a system.
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Examples of Expert Systems
1965 DENDRAL Stanford analyze mass spectrometry data1965 MACSYMA MIT symbolic mathematics problems
1972 MYCIN Stanford diagnosis of blood diseases
1972 Prospector SRI Mineral Exploration
1975 Cadeceus U. of PITT Internal Medicine
1978 DIGITALISMIT digitalis therapy advise
1979 PUFF Stanford obstructive airway diseases
1980 R1 CMU computer configuration
1982 XCON DEC computer configuration
1983 KNOBS MITRE mission planning
1983 ACE AT&T diagnosis faults in telephone cables
1984 FAITH JPL spacecraft diagnosis
1986 ACES Aerospace satellite anomaly diagnosis
1986 Cambpell diagnose cooker malfunctions
1986 DELTA/CATS GE diagnosis of diesel locomotives
1987 AMEX credit authorization
1992 MAX NYNEX telephone network troubleshooting
1995 Caltech PacBell network management
1997 UCI planning drug treatment for HIV
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END
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