Expert system : Examples

Expert system : Examples

CS 331/531 Dr M M Awais 2

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“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 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

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

Various tasks could be performed A layout presented by Hayes-Roth

& colleagues in 1983 is presented here


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Diagnosis

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

MYCIN - diagnosed blood infection. Shortliffe, 1976.


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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

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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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

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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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

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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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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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

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

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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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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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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MYCIN Description of the system.

Written in LISP. MYCIN was a mixed-chaining

production system.


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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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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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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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MYCINA Consultation with MYCIN@ mycinMYCIN 26-MarchInstructions? (Y or N) N26-March 10:42-----PATIENT-1-----Patient's name: Yousaf


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Consultation with MYCINAge? 34Sex? 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 MYCINSex? MaleRace? 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 MYCIN

Are there any pending cultures for Yousaf?

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

Cerebro Spinal FluidPlease give the date and time when the pending CSF culture was obtained.


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Consultation with MYCIN 26-March-7:00

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

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

NoAre there any other pending cultures?


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Consultation with MYCIN

NoHas Yousaf recently had symptoms of

persistent headache or other abnormal neurological symptoms (dizziness, lethargy, etc)?

YesHas 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 YesThe 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 10:00Has Yousaf had neurosurgery?


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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 MYCINIt 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 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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Has Yousaf had neurosurgery? How [3] [i.e. HOW was it established

that the type of infection is bacterial?]


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

Rule Yes No500 (.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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Has Yousaf had neurosurgery? NoIs Yousaf a burn patient? Yes


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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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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 MYCININFECTION-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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[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) GENTAMICINDose: 131mg(3.2ml) q8h IV


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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

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 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 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

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/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/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.


RBS 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/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/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 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-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-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).