Expert systems can either support decision makers or completely replace them.
Expert systems are the most widely applied & commercially successful AI technology.
Prof. Edward Feigenbaum of Stanford University, leading researchers in ES has produced the following definition:
" . . . An intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution."
Expertise is the extensive, task-specific knowledge acquired from training, reading, and experience.
The transfer of expertise from an expert to a computer and then to the user involves four activities:
knowledge acquisition from experts or other sources.
knowledge representation in the computer.
knowledge inferring, resulting in a recommendation for novices.
knowledge transfer to the user.
CASE: GE Models Human Troubleshooters Problem:
GE wanted an effective & dependable way of disseminating expertise to its engineers & preventing valuable knowledge from “retiring” from the company.
Solution:
GE decided to build an expert system that modeled the way a human troubleshooter works.
The system builders spend several months interviewing an employee & transfer their knowledge to a computer.
The new diagnostic technology enables a novice engineer to uncover a fault by spending only a few minutes at the computer terminal.
Results:
The system is currently installed at every railroad repair shop served by GE.
Conventional Systems
1. Knowledge and processing are combined in one sequential program.
2. Programs do not make mistakes (only programmers do).
3. Do not usually explain why input data are needed or how conclusions were drawn.
4. The system operates only when it is completed.
5. Execution is done on a step-by-step (algorithmic) basis Execution is done by using heuristics and logic.
6. Needs complete information to operate
7. Effective manipulation of large databases.
8. Efficiency is a major goal.
9. Easily deals with quantitative data
Expert Systems
1. Knowledge base is clearly separated from the processing (inference) mechanism (knowledge rules are separated from the control).
2. Program may make mistakes.
3. Explanation is a part of most expert systems.
4. The system can operate with only a few rules (as a first prototype)
5. Can operate with incomplete or uncertain information
6. Effective manipulation of large knowledge bases
7. Representation and use of data Representation and use of
knowledge .
8. Effectiveness is a major goal
9. Easily deals with qualitative data 9
The Structure of ES
THE KNOWLEDGE BASE (LONG TERM MEMORY)
The key bottleneck in developing an expert system.
Contain everything necessary for understanding, formulating and solving a problem.
It contains facts and heuristics.
The most popular approach to representing domain knowledge is using production rules.
Rule 1
IF car won't start THEN problem in electrical system
Rule 2
IF problem in electrical system AND battery voltage is below 10 volts THEN bad battery.
WORKING MEMORY (SHORT TERM MEMORY)
Contains facts about a problem that are discovered during consultation with the expert system.
System matches this information with knowledge contained in the knowledge base to infer new facts.
The conclusion reach will enter the working memory.
Thus, working memory contain information either supplied by user or infer by the system.
INFERENCE ENGINE
Knowledge processor which is modeled after the expert reasoning power.
Processor in an expert system that matches the facts contained in the working memory with the domain knowledge contained in the knowledge base, to draw conclusion about the problems.
It taps the knowledge base and working memory to derive new information and solve problems.
THE USER INTERFACE
The user communicates with the expert system through the user interface.
It allows the user to query the system, supply information and receive advice.
The aims are to provide the same form of communication facilities provided by the expert.
But normally has less capability of understanding natural language and general knowledge.
THE EXPLANATION FACILITY
A trademark of expert systems: ability to explain their reasoning.
An additional component of expert system.
ES can provide explanation on:
WHY it is asking the question
HOW it reached some conclusion.
Explaining WHY
Example:
MM : Will the car not start?
Person : WHY
MM : If I know that the car won't start then I usually assume the problem is the electrical system.
Expert responds with what they might conclude from the answer.
ES respond to a WHY query by displaying the rule it is
currently pursuing.
Explaining HOW
Besides giving final results, expert system can explain howit arrived at a result.
Example:
MM : The battery is bad
Person : HOW
MM : Since your car won't start, I assumed there was a problem with the electrical system. I found the battery voltage was below 10, I knew the battery was bad.
ES respond by tracing back through the rules that fire the conclusion.
This tracing is a map of the system line of reasoning.
Characteristics of Expert Systems
1. High-level expertise.
The most useful characteristic of an expert system.
This expertise can represent the best thinking of top experts in the field, leading to problem solutions that
are imaginative, accurate, and efficient.
2. Adequate response time.
The system must also perform in a reasonable amount of time, comparable to or better than the time
required by an expert to solve a problem.
3. Permits Inexact Reasoning.
These types of applications are characterized by information that is uncertain, ambiguous, or unavailable and by domain knowledge that is inherently inexact.
4. Good Reliability.
The system must be reliable and not prone to crashes because it will not be used 20
5. Comprehensibility.
The system should be able to explain the steps of its reasoning while executing so that it is understandable.
The systems should have an explanation capability in the same way that human experts are suppose to be
able to explain their reasoning.
6. Flexibility.
Because of the large amount of knowledge that an expert system may have, it is important to have an
efficient mechanism for modifying the knowledge base.
7. Symbolic Reasoning.
Expert systems represent knowledge symbolically as sets of symbols that stand for problems concepts.
These symbols can be combined to express relationship between them. When these relationship are represented in a program they are called symbol structures.
For example,
Assert: Ahmad has a fever
Rule: IF person has fever THEN take panadol
Conclusion: Ahmad takes panadol
8. Reasons Heuristically
Experts are adapt at drawing on their experiences to help them efficiently solved some current problem.
Typical heuristics used by experts:
I always check the electrical first.
People rarely get a cold during the summer
If I suspect cancer, then I always check the family history.
9. Makes Mistakes
Expert systems can make mistakes.
Since the knowledge of expert have to be captured as close as possible in expert system, like its human
counterpart, it can make mistakes.
10. Thrives on Reasonable Complexity
The problem should be reasonably complex, not too easy or too difficult.
11. Focuses Expertise
Most experts are skillful at solving problems within their narrow area of expertise, but have limited ability
outside this area.
ES Development Life Cycles (ESDLC)
ESDLC contains the following phases:
1. Assessment
2. Knowledge Acquisition
3. Design
4. Testing
5. Documentation
6. Maintenance
Phase 1
Assessment
Phase 2
Knowledge Acquisition
Phase 3
Design
Phase 4
Test
Phase 5
Documentation
Phase 6
Maintenance
Requirements
Knowledge
Structure
Evaluation
Product
Refinements
Explorations
Reformulations
1. Assessment
Determine feasibility & justification of the problem
Define overall goal and scope of the project
Resources requirement
Sources of knowledge
2. Knowledge Acquisition
Acquire the knowledge of the problem
Involves meetings with expert
Bottleneck in ES development
3. Design
Selecting knowledge representations approach and problem solving strategies
Defined overall structure and organization of system knowledge
Selection of software tools
Built initial prototype
Iterative process
4. Testing
Continual process throughout the project
Testing and modifying system knowledge
Study the acceptability of the system by end user
Work closely with domain expert that guide the growth of the knowledge and end user that guide in user interface design
5. Documentation
Compile all the projects information into a document for the user and developers of the system such as:
User manual
diagrams
Knowledge dictionary
6. Maintenance
Refined and update system knowledge to meet current needs
Participants in ES Development
The main participants in the process of
building an expert system are:
a. the domain expert
b. the knowledge engineer
c. the user.
THE DOMAIN EXPERT
Is a person who has the special knowledge, judgment, experience, skills and methods, to give advice and solve problems in a manner superior to others.
Although an expert system usually models one or more experts, it may also contain expertise from other sources such as books and journal articles.
Qualifications needed by the Domain Expert:
Has expert knowledge
Has efficient problem-solving skills
Can communicate the knowledge
Can devote time
Must be cooperative
If you call someone an “expert” for a project, treat that person like one. Even if the person doesn’t know
everything about the domain, the person knows more than you.
Patrick E. Dessert
THE KNOWLEDGE ENGINEER
A person who designs, builds and tests an expert systems.
Qualifications needed by Knowledge Engineer:
Has knowledge engineering skills (art of building expert system)
Has good communications skills
Can match problems to software
has expert system programming skills
A KNOWLEDGE ENGINEER
I have been working as knowledge engineer for a software house for two years. Each project is different. The job is challenging and requires creative thinking and strong communication skills. I
started as junior knowledge engineer at a salary of $30,500. I am now a lead engineer with a salary of $40,700 plus a nice annual bonus
Christine Melekian
THE USER
Is a person who uses the expert system once it is developed.
Can aid in knowledge acquisition (giving broad understanding of the problems)
Can aid in system development
When to Use Expert Systems
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
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
Justifying the Problem Domain
The first step toward successful system is to pick the right problem and justify its selection.
Selecting the right problem should be the first consideration in ES development.
This step entails identifying the domain expert,the user and the payoff from the system 44
Key Domain Characteristics:
A narrow, well defined focus
Moderate solution time
Symbolic knowledge and reasoning
A stable domain
Size of the knowledge base (100 rules for first-time domain)
Available test cases
Complexity of the domain
Degree of uncertainty or fuzziness
Demonstration of worth
Scarce expertise
Appropriate depth of required knowledge
MYCIN: A medical expert system
Developed at Stanford University in the mid 1970's.
The first large expert system that perform at thelevel of human expert and use as benchmark by
expert system developers.
Provide consultative advise about bacteremia and meningitis.
Bacteremia is an infections that involve bacteria in the blood.
Meningitis is an infection which is an inflammation of the membranes that envelop the brain and spinal cord.
Can be fatal, thus need quick response, but positive identification normally takes 24 - 48 hours.
Normally doctors must begin treatment in the absence of lab results.
Very complex and doctor need advice of an expert of bacteremia and meningitis.
How MYCIN works?
MYCIN begin by initiating a dialogue.
Physician response to the questions
MYCIN provides:
a. diagnosis
b. prescription 48
How MYCIN reasons?
Laboratory results of body fluid analyses
Symptoms that patient is displaying
Characteristics of the patient such as age, sex etc.
MYCIN consultative proceeds in 2 phases:
Diagnosis is made to identify the most likely infection organisms
Prescribe one or more drugs (antibiotics)
MYCIN: Example of a rule
IF the stain of the organism is gram-positive AND the morphology of the organism is coccus,
AND the growth conformation of the organism is clumps,THEN there is suggestive evidence (0.7) that the identity of the organism is staphyloccus
A consultation With MYCIN
1. Please enter information about the patient.
Name Age Sex Race
Sally 42 years Female Melayu
2. Are there any positive cultures obtained from SALLY?
No
3. Are there any pending cultures of SALLY?
Yes
4. Site: date collected: stain examined: method
CSF 3-Jun-75 Yes N/A
5. Stain of smear:
(type NONE if no organism was seen on the smear)
None
(Unable to reach conclusion from lab test, like physician
continue asking questions.)
6. Has Sally recently had symptoms of persistent headache or other abnormal neurologic symptoms(dizziness, lethargy etc.)
Yes
7. Has SALLY recently had objective evidence of abnormal neurologic signs documented by physician observation of examination?
Yes
Questions 6 and 7 traditional approach to diagnose by
symptoms.
Conclude from the questions by giving first conclusion.
Conclusion: The CSF cultures will be considered to be
associated with MENINGITIS.
The following questions will identify particular organism that
cause the infections.
8. Is the meningitis a hospital-acquired infection?
Yes
9. Is SALLY clinical history consistent with EPIGLOTTITIS?
No
10. Is Sally clinical history consistent with OTITIS-MEDIA?
No
11. Has the patient ever been to an area that is endemic for
coccidiomycoses?
Unknown
12. Is Sally a burn patient?
Yes (Why)
Conclusion: The type of infection may be BACTERIAL.
The final phase, prescription of antibiotics.
13. Does SALLY have clinically significant allergic
reaction to any antimicrobial agent?
No
14. Is Sally pregnant of breast feeding?
No
15. Do you have reason to suspect that SALLY may
have impaired renal functions?
No
16. SALLY weight in kilograms?
51.4
preferred therapy recommendation is as follows:
Give the following combination:
1. ETHAMBUTAL
Dose: 1.28g (13.0 100mg tablets) q24h PO for 60 days then 770 mg (7.5 100 mg tablets) q24h PO.
Comments: periodic vision screening tests are recommended for optic neuritis.
2. INH
Dose: 513 mg (5.0 100mg-tablets) q24h PO
3. RIFAMPIN
Dose: 600 mg PO q24h Comments: Administer dose on empty stomach.
HOW GOOD AN EXPERT IS MYCIN?
In one complex evaluation, 8 independent evaluators evaluate MYCIN with 9 others human diagnostician for 10 difficult cases of meningitis.
The task used was the selection of drugs for cases of
meningitis before causative agents had been identified.
Two phases of the evaluation:
a. MCYIN and 9 human experts evaluate 10 cases
b. Each of them prescribe medications 57
Two evaluative criteria was used to see whether the prescriptions:
a. Would be effective against the actual bacteria
after it was finally identified.
b. Adequately covered for other possible bacteria while avoiding over-prescribing.
Results:
Criteria 1: MYCIN and 3 other humans expert consistently prescribe therapy that would have been effective for all 10 cases.
Criteria 2: MYCIN received higher ratings. 65% correct in all the cases whereas human expert
42.5% to 62.5%. 59
MYCIN strengths is based on 4 factors:
a. MYCIN's knowledge base is extremely detail because acquired from the best human practitioners.
b. MYCIN do not overlook anything or forget any details. It considers every possibility.
c. MYCIN never jumps to conclusions of fails to ask for key pieces of information.
d. MYCIN is maintained at a major medical center and consequently, completely current.
MYCIN represents
man-years of effort.
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Building Tools
Since mid-1970s, a wide choice of tools and approaches fro developing ES have become available.
They range from high-level and AI programming language to shells to ready-to-use customized packages for industry and government.
Which tools to adopt depend on:
The nature of the problem
The skill of the builder
The function it is expect to perform (either diagnoses or monitoring)