Transcript Introduction to knowledge

Some expert system
case histories
“Classic” systems
This refers to the early, pre-1980,
systems that demonstrated what was
possible with the new technology.
 They showed that it was possible to
 capture heuristic knowledge and store
it;
 make a computer that could dispense
the advice that, previously, only an
expert human could provide.

“Classic” systems

As a result, techniques used in these
early systems were copied in very many
subsequent systems, and in many
expert system shells.
Some classic 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

Some influential systems that
came later
CENTAUR
 INTERNIST
All medical expert systems
 PUFF
 CASNET
 DELTA
- locomotive engineering
 Drilling Advisor
- oilfield prospecting
 ExperTax
- tax minimisation advice
 XSEL
- computer sales

}
A classification of expert
system tasks
The following is a classification of expert
systems, in terms of the kinds of task
that they have been designed to
perform.
 It was drawn up by Hayes-Roth &
colleagues in 1983.

A classification of expert
system tasks

Diagnosis. The process of finding faults
in a system, or diseases in a living
system.
example: MYCIN - diagnosed blood
infection. Shortliffe, 1976.
A classification of expert
system tasks

Interpretation. The analysis of data, to
determine their meaning.
example: PROSPECTOR interpreted geological data as potential
evidence for mineral deposits. Duda,
Hart, et al 1976.
A classification of expert
system tasks

Monitoring. The continuous
interpretation of signals from a system,
so that a diagnosis or an alarm can be
given when required.
example: NAVEX - monitored radar
data and estimated the velocity and
position of the space shuttle. Marsh,
1984
A classification of expert
system tasks

Design. The production of specifications
from which systems, satisfying particular
requirements, can be made.
example: R1/XCON - configured VAX
computer systems on the basis of
customers' needs. McDermott, 1980.
A classification of expert
system tasks

Planning. The production of a sequence
of actions that will achieve a particular
goal.
example: MOLGEN - planned
chemical processes whose purpose was
to analyse and synthesise DNA. Stefik,
1981.
A classification of expert
system tasks

Instruction. Teaching a student a body of
knowledge, varying the teaching
according to assessments it makes of
the student's current knowledge. N.B.
This type of expert system is often
called an intelligent tutoring system.
example: SOPHIE - instructed the
student on the repair of an electronic
power-pack. Brown, Burton & de Kleer,
1982.
A classification of expert
system tasks

Prediction. Forecasting future events,
using a model based on past events.
example: PLANT - predicted the
damage to be expected when a corn
crop was invaded by black cutworm.
Boulanger, 1983.
A classification of expert
system tasks

Debugging & repair. Generating and,
perhaps, administering remedies for
system faults.
example: COOKER ADVISER provides repair advice with respect to
canned soup sterilising machines. Texas
Instruments, 1986.
A classification of expert
system tasks

Control. Governing the behaviour of a
system by anticipating problems,
planning solutions, and monitoring
actions.
example: VENTILATOR
MANAGEMENT ASSISTANT scrutinised the data from hospital
breathing-support machines, and
provided accounts of the patients'
conditions. Fagan, 1978.
A system that performed
diagnosis: MYCIN
Knowledge domain: diagnosing blood
infections of the sort that might be
contracted in hospital
 Written by: Edward Shortliffe and
colleagues, 1972 to late 1970s.

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

MYCIN
Knowledge representation: production
rules (and simpler data structures)
 Inference engine: Mixed chaining, but
principally backward chaining from a top
goal: that diagnosis & therapy is needed.
Rules are found to satisfy conditions of this
rule, then further rules to satisfy these.
Evidence may be sought from the user.
 Dealing with uncertainty: By calculating
certainty factors.

MYCIN
Significance: Much copied in later systems.
 A successful (and enormously influential) expert
system:




did a complex task.
performed well: tested against medical students,
non-specialist doctors and blood infection
specialists, it did better than the former two
groups and equalled the latter group.
but note that MYCIN was just a laboratory
demonstration - it was never marketed, or
installed in a hospital and used for routine work.
MYCIN
A Consultation with MYCIN
@ mycin
MYCIN 26-March
Instructions? (Y or N)
N
26-March 10:42
-----PATIENT-1----Patient's name:
 Alan Apt
A 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.
A Consultation with MYCIN
Sex?
 Male
Race?
 Caucasian
Are there any cultures for Alan Apt that
may be related to the present illness, and
from which organisms have been grown
successfully in the lab?
 No
A Consultation with MYCIN
Are there any pending cultures for Alan
Apt?
 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.
A 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?

A Consultation with MYCIN
No
Has Alan Apt recently had symptoms of
persistent headache or other abnormal
neurological symptoms (dizziness, lethargy,
etc)?
 Yes
Has Alan Apt recently had objective evidence of
abnormal neurological signs (nuchal rigidity,
coma, seizures, etc) documented by a
physician?

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 Alan Apt had neurosurgery?

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.

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
A Consultation with MYCIN
Therefore, if
[4] Alan Apt has had neurosurgery, and

[5] the time since the neurosurgical operation is
less than two months, and

[6] the type of neurosurgery is not ventricularurethal 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]

A Consultation with MYCIN
Has Alan Apt had neurosurgery?
 How [3]
 [i.e. HOW was it established that the type
of infection is bacterial?]
A Consultation with MYCIN
The following rules concluded about the
likelihood that the type of infection is
bacterial:
Rule Yes
No
500
(.4)
501
(.17)
502
(.1)
524
(.42)
504
(.28)
Thus it has been established that the type of
infection might be bacterial.
A Consultation with MYCIN
Has Alan Apt had neurosurgery?
 No
Is Alan Apt a burn patient?
 Yes
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.
A Consultation with MYCIN
Does Alan Apt have focal neurological
signs?
 No
[Determining which dosages are desirable
for use against the e.coli...]
[Determining which drugs are desirable for
use ...]
A Consultation with MYCIN
INFECTION-1 is MENINGITIS
+ <ITEM-1> DIPLOCOCCUSPNEUMONIAE (from clinical evidence
only)
+ <ITEM-2> E.COLI (from clinical
evidence only)
+ <ITEM-3> PSEUDOMONAS
AERUGINOSA (from clinical evidence
only)
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
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
MYCIN

Description of the system.
 Written in LISP.
 MYCIN was a mixed-chaining
production system.
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.
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.
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.
A system that performed
prescription: 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.

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.

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.
A system that performed
configuration/design: 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.

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.

R1/XCON

Significance:
A rather simple forward-chaining rulebased expert system, which
nevertheless performed well, solved a
difficult manufacturing problem, and
proved to be enormously profitable.
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.
R1/XCON

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.
R1/XCON
Each configuration was taking 25 minutes,
and orders were arriving at a rate of 10,000 a
year.
 There was a high level of errors in the
configurations produced.

R1/XCON
DEC attempted to write a conventional
program to do this task, with no success,
then invited McDermott to write an AI system
to do it. 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.

R1/XCON

However, R1/XCON suffered from the
shortcomings of simple production-rulebased 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.
A system that performs planning:
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.

OPTIMUM-AIV
Unlike a conventional scheduling tool, it
has a knowledgebase which describes
the underlying causal links that dictate
that the assembly must be undertaken in
a particular order.
 Therefore, if a plan fails and has to be
repaired, the system can make
intelligent decisions about which
alternative plans will work and which will
not.

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).
