Expert system examples
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Expert system : Examples. “Classic” systems. Pre-1980s Systems showed how to capture heuristic knowledge and store it; make a software that could mimic advice dispensation like expert human do. Techniques that were implemented were used in many subsequent systems,

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Expert system : Examples

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Expert system examples

Expert system : Examples


Classic systems

“Classic” systems

  • Pre-1980s

  • Systems showed how to

    • capture heuristic knowledge and store it;

    • make a software that could mimic advice dispensation like expert human do.

  • Techniques that were implemented were used in many subsequent systems,

  • Many expert system shells were developed.

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Expert systems

Expert systems

  • MACSYMA

    • advised the user on how to solve complex maths problems.

  • DENDRAL

    • advised the user on how to interpret the output from a mass spectrograph

  • MYCIN

  • PROSPECTOR

  • R1/XCON

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Others

Others

  • CENTAUR

  • INTERNIST

  • PUFF

  • CASNET

  • DELTA - locomotive engineering

  • Drilling Advisor - oilfield prospecting

  • ExperTax - tax minimisation advice

  • XSEL - computer sales

All medical expert systems

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Task classification

Task Classification

  • Various tasks could be performed

  • A layout presented by Hayes-Roth & colleagues in 1983 is presented here

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Diagnosis

Diagnosis

  • finding faults in a system, or diseases in a living system

    MYCIN - diagnosed blood infection. Shortliffe, 1976.

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Interpretation

Interpretation

  • The analysis of data, to determine their meaning

    PROSPECTOR - interpreted geological data as potential evidence for mineral deposits. Duda, Hart, et al 1976.

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Monitoring

Monitoring

  • The continuous interpretation of signals from a system for avoiding dangerous situations

    NAVEX - monitored radar data and estimated the velocity and position of the space shuttle. Marsh, 1984

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Expert system examples

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Design

Design

  • To ensure production of specifications, satisfying particular requirements

    R1/XCON - configured VAX computer systems on the basis of customers' needs. McDermott, 1980.

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Planning

Planning

  • Production of a sequence of actions that will achieve a particular goal.

    MOLGEN - planned chemical processes whose purpose was to analyse and synthesise DNA. Stefik, 1981.

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Expert system examples

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Instruction intelligent tutoring systems

Instruction: Intelligent Tutoring Systems

  • Teaching a student a body of knowledge, varying the teaching according to assessments

    SOPHIE - instructed the student on the repair of an electronic power-pack. Brown, Burton & de Kleer, 1982.

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Prediction

Prediction

  • Forecasting future events, using a model based on past events.

    PLANT - predicted the damage to be expected when a corn crop was invaded by black cutworm. Boulanger, 1983.

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Expert system examples

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Debugging repair

Debugging & repair

  • Generating, administering remedies for system faults.

    COOKER ADVISER - provides repair advice with respect to canned soup sterilising machines. Texas Instruments, 1986.

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Expert system examples

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Controls

Controls

  • Governing the behaviour of a system by anticipating problems, planning solutions, and monitoring actions.

    VENTILATOR MANAGEMENT ASSISTANT - scrutinised the data from hospital breathing-support machines, and provided accounts of the patients' conditions. Fagan, 1978.

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Mycin diagnosis system

MYCIN: Diagnosis System

  • Domain: diagnose blood infections of the sort that might be contracted in hospital

  • Developed by: Edward Shortliffe and colleagues, 1972 to late 1970s.

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Mycin

MYCIN

  • Purpose:to assist a physician, who was not an expert in the field of antibiotics, with the diagnosis & treatment of blood disorders (and in particular to establish whether the patient was suffering from a serious infection like meningitis).

  • Input: symptoms & test results

  • Output: a diagnosis, accompanied by a degree of certainty, & recommended therapy

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Mycin1

MYCIN

  • Knowledge representation: production rules

  • Inference engine: Mixed chaining, but principally backward chaining from a top goal

  • Dealing with uncertainty: By calculating certainty factors.

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Mycin2

MYCIN

  • A Complete system that did complex task.

  • Performed better than medical students and non-specialist doctors.

  • Performed equally good to blood infection specialist doctors

  • MYCIN was a laboratory demonstration only

  • Was never commercialised, was never marketed, or installed in a hospital and used for routine work.

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Mycin3

MYCIN

  • Description of the system.

    • Written in LISP.

    • MYCIN was a mixed-chaining production system.

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Mycin4

MYCIN

  • The sequence of operations was that the system asked:

    • Questions to get general details about the patient (name, age, sex, race, clinical test results already known).

    • Questions designed to find a possible, general diagnosis (e.g. the patient has probably got some form of meningitis), by forward chaining.

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Mycin5

MYCIN

  • Questions designed to test this theory, and establish specific details, by backward chaining. This was where most of the reasoning was done.

  • Questions designed to produce a recommended treatment, again by forward chaining.

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Mycin6

MYCIN

  • MYCIN could explain its reasoning in a rather simple way:

    • when asked "Why do you think that is the diagnosis?”, MYCIN listed the rules it had applied, in reverse order, with CFs.

    • When asked "Why do you want to know that?", MYCIN described the rule it was trying to execute, and what value it was trying to find.

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Mycin7

MYCIN

A Consultation with MYCIN

@ mycin

MYCIN 26-March

Instructions? (Y or N)

  • N

    26-March 10:42

    -----PATIENT-1-----

    Patient's name:

  • Yousaf

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Consultation with mycin

Consultation with MYCIN

Age?

  • 34

    Sex?

  • Why

  • [i.e. Why is it important to determine the sex of PATIENT-1?]

    The sex of the patient is one of the four standard parameters that identify a patient. This may be useful later.

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Consultation with mycin1

Consultation with MYCIN

Sex?

  • Male

    Race?

  • Sindhi

    Are there any cultures for Yousaf that may be related to the present illness, and from which organisms have been grown successfully in the lab?

  • No

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Consultation with mycin2

Consultation with MYCIN

Are there any pending cultures for Yousaf?

  • Yes

    -----CULTURE-1-----

    From what site was the specimen for CULTURE-1 taken?

  • Cerebro Spinal Fluid

    Please give the date and time when the pending CSF culture was obtained.

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Consultation with mycin3

Consultation with MYCIN

  • 26-March-7:00

    Was a smear examined at the time the specimen was obtained?

  • Yes

    Were there any cultures seen on the smear of the specimen?

  • No

    Are there any other pending cultures?

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Consultation with mycin4

Consultation with MYCIN

  • No

    Has Yousaf recently had symptoms of persistent headache or other abnormal neurological symptoms (dizziness, lethargy, etc)?

  • Yes

    Has Yousaf recently had objective evidence of abnormal neurological signs (nuchal rigidity, coma, seizures, etc) documented by a physician?

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A consultation with mycin

A Consultation with MYCIN

  • Yes

    The CSF cultures will be considered to be associated with MENINGITIS.

    Please give the date on which clinical evidence (symptoms, signs, or laboratory tests) of the meningitis first appeared.

  • 25 March 10:00

    Has Yousaf had neurosurgery?

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A consultation with mycin1

A Consultation with MYCIN

  • Why

  • [i.e. WHY is it important to determine whether PATIENT-1 has had neurosurgery?]

    This will aid in determining the organisms (other than those seen on cultures or smears) that might be causing the infection.

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A consultation with mycin2

A Consultation with MYCIN

It has already been established that

  • [1] the culture that requires therapy is meningitis, and

  • [2] organisms were not seen on the stain of the pending csf culture (CULTURE-1), and

  • [3] the type of infection is bacterial

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A consultation with mycin3

A Consultation with MYCIN

Therefore, if

  • [4] Yousaf has had neurosurgery, and

  • [5] the time since the neurosurgical operation is less than two months, and

  • [6] the type of neurosurgery is not ventricular-urethal shunt

    then

  • there is evidence that the organisms that might be causing the infection are staphylococcus-coag-pos (0.75), staphylococcus-coag-neg (.5), e.coli (.4), klebsiella (0.3)

  • Author: Yu [RULE511]

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A consultation with mycin4

A Consultation with MYCIN

Has Yousaf had neurosurgery?

  • How [3]

  • [i.e. HOW was it established that the type of infection is bacterial?]

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A consultation with mycin5

A Consultation with MYCIN

The following rules concluded about the likelihood that the type of infection is bacterial:

RuleYesNo

500(.4)

501(.17)

502(.1)

524(.42)

504(.28)

Thus it has been established that the type of infection might be bacterial.

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A consultation with mycin6

A Consultation with MYCIN

Has Yousaf had neurosurgery?

  • No

    Is Yousaf a burn patient?

  • Yes

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A consultation with mycin7

A Consultation with MYCIN

I have found evidence (RULE507) that treatment should cover for Diplococcus pneumoniae causing meningitis.

I have found evidence (RULE545) that treatment should cover for e.coli causing meningitis.

I have found evidence (RULE545 RULE578) that treatment should cover for Pseudomonas aeruginosa causing meningitis.

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A consultation with mycin8

A Consultation with MYCIN

Does Yousaf have focal neurological signs?

  • No

    [Determining which dosages are desirable for use against the e.coli...]

    [Determining which drugs are desirable for use ...]

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A consultation with mycin9

A Consultation with MYCIN

INFECTION-1 is MENINGITIS

+ <ITEM-1> DIPLOCOCCUS-PNEUMONIAE (from clinical evidence only)

+ <ITEM-2> E.COLI (from clinical evidence only)

+ <ITEM-3> PSEUDOMONAS AERUGINOSA (from clinical evidence only)

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A consultation with mycin10

A Consultation with MYCIN

[REC-1] My preferred therapy recommendation is as follows:

In order to cover for items <1 2 3>:

Give the following in combination:

1) AMPICILLIN

Dose: 3.85g(30.8ml) q4h IV

2) GENTAMICIN

Dose: 131mg(3.2ml) q8h IV

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A consultation with mycin11

A Consultation with MYCIN

Comments: monitor serum concentrations.

Since high concentrations of penicillins can inactivate aminoglycosides, do not mix these antibiotics in the same IV bottle.

Do you wish to see the next choice therapy?

  • No

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Crop advisor

CROP ADVISOR

  • Developed by ICI (in 1989) to advise cereal grain farmers on appropriate fertilisers and pesticides for their farms.

  • The choice of chemical, amount, and time of application depends on such factors as crop to be grown, previous cropping, soil condition, acidity of soil, and weather.

  • Farmers can access the system via the internet.

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Crop advisor1

CROP ADVISOR

  • Given relevant data, the system produces various financial return projections for different application rates of different chemicals.

  • The system uses statistical reasoning to come to these conclusions.

  • If the question asked is outside the system's expertise, it refers the caller to a human expert.

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Crop advisor2

CROP ADVISOR

  • The chief advantages of this system have been

    • that employees at ICI have been relieved of the need to provide lengthy telephone advice sessions,

    • and the quality of the advice has become much more uniform, which has increased confidence in the company's products.

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R1 xcon

R1/XCON

  • Knowledge domain: Configuring VAX computers, to customers' specifications.

  • Written by: John McDermott and colleagues, 1978 - 1981

  • Input: Required characteristics of the computer system.

  • Output: Specification for the computer system.

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R1 xcon1

R1/XCON

  • Knowledge representation: Production rules.

  • Inference engine: Forward chaining: the output specification was assembled in working memory.

  • Dealing with uncertainty: No mechanism for this: the system simply assembled one answer, assumed to be good enough to do the job.

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R1 xcon2

R1/XCON

  • Significance:

    A rather simple forward-chaining rule-based expert system, which performed well, solved a difficult manufacturing problem, and proved to be enormously profitable.

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R1 xcon3

R1/XCON

  • Digital Equipment Corporation's problem was that they were marketing the best-selling Vax-11 series of computers, and the department responsible for configuration was failing to keep up with customer demand.

  • Each computer was the result of a consultation between a sales executive and the customer, designed to discover the customer's requirements, after which a configuration was drawn up, from which the system was built.

  • Each configuration was taking 25 minutes, and orders were arriving at a rate of 10,000 a year.

  • High error rate in the configurations was recorded.

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R1 xcon4

R1/XCON

  • DEC tried a conventional program to solve this problem, with no success, then asked McDermott to write an AI system.

  • McDermott wrote R1/XCON.

  • By 1986, it had processed 80,000 orders, and achieved 95-98% accuracy.

  • It was reckoned to be saving DEC $25M a year.

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R1 xcon5

R1/XCON

  • However, R1/XCON suffered from the shortcomings of simple production-rule-based systems.

    • When the nature of the task changed, fresh rules were simply added at the end of the rulebase.

    • Soon, the rulebase was very large, unreliable and incomprehensible.

    • Expensive rewriting was needed to restore the operation of the system.

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Optimum aiv

OPTIMUM-AIV

  • OPTIMUM-AIV is a planner used by the European Space Agency (1994) to help in the assembly, integration, and verification of spacecraft.

  • It generates plans, and monitors their execution.

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Optimum aiv1

OPTIMUM-AIV

  • it has a knowledgebase that describes the causal links that describe that in what particular order the assembly must be undertaken.

  • Also, if a plan fails and has to be repaired, the system can make intelligent decisions about the alternative plans that will work and will not.

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Optimum aiv2

OPTIMUM-AIV

  • It can engage in hierarchical planning - this involves producing a top-level plan with very little detail, and then turning this into increasingly more detailed lower-level plans.

  • It can reason about complex conditions, time, and resources (such as budget constraints).

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