1D28
.M414
^o.
1630-
EXPERT SYSTEMS AND EXPERT
SUPPORT SYSTEMS
THE NEXT CHALLENGE FOR MANAGEMENT
:
Fred
l.
Luconi
Thomas W. Malone
Michael S. Scott Morton
December 198A
CISR WP #122
Sloan wp #1630-85
Center for Information Systems Research
Massachusetts
Institute of
Technology
Sloan School of Management
77 Massachusetts Avenue
Cambridge, Massachusetts, 02139
EXPERT SYSTEMS AND EXPERT
SUPPORT SYSTEMS
THE NEXT CHALLENGE FOR MANAGEMENT
:
Fred
l.
Luconi
Thomas W, Malone
licHAEL S. Scott Morton
December 1984
CISR WP #122
Sloan wp #1630-85
©
F.L.
LucoNiy T.W. Malone^ M.S. Scott Morton
Center for Information Systems Research
Sloan School of Management
Massachusetts Institute of Technology
198^1
EXPERT SYSTEMS AND EXPERT SUPPORT SYSTEMS;
THE NEXT CHALLENGE FOR MANAGEMENT
Fred
Luconi
Applied Expert Systems,
L.
Inc.
Thomas W. Mai one
Alfred P. Sloan School of Management
Massachusetts Institute of Technology
Michael S. Scott Morton
Alfred P. Sloan School of Management
Massachusetts Institute of Technology
INTRODUCTION
this
In
finding
ways
silicon
age
of
to
learn
the
profitably
revolution",
include
managers
effective
myriad
the
use
applications
These
chip.
and
"microchip
applications
of
the
office
computers,
personal
are
automation, roootics, computer graphics, and the various forms of broad-band
narrow-band
and
communication.
applications to emerge from the
world
of
business
In
of
most
the
research
Expert Systems
is
Systems describes the technical
small
One
labs
intriguing
and
move
into
of
the
these
new
practical
Most literature about Expert
(E.S.).
concepts upon which they are based and the
number of systems already in use.
this article we shift this focus and discuss how these systems can be
used in a broad range of business applications.
We will
business
be
applications,
Expert System is
Instead,
we
the
knowledge
that
not sufficient to make
believe
that
Expert Support Systems
our
(E.S.S.)
focus
can
argue that in many
feasibly
encoded
satisfactory decisions
should
that will
increasingly
aid,
rather
on
be
than
by
in
an
itself.
designing
replace,
human
decision makers.
After
expansion
briefly
of
problem-solving.
defining
a
classical
a
We
then
use
few
Expert
framework
this
Systems
we
understanding
for
framework
concepts,
to
identify
the
offer
an
managerial
limits
of
current expert systems and decision support systems technology and show how
expert support systems can be seen as the next logical step in both fields.
-2-
BASIC CONCEPTS
Artificial
defined,
Broadly
Intelligence
(A.I.)
involves the design and construction of computer systems
the
level
intelligent
of
human
behavior.
can perform at
Vr.?'c
prospect
The
machines
of
(see
particularly
is
no
v.'ith
respect
to
and
progress
in
doubt the A.I.
about
claims
the
machine
been
has
oversold,
qi^ossly
language
racural
this
business
"hype" and the inevitable backlash that is just beginning,
it is an
understandi'i'-;,
journal
There
6).
3.
that
in the popular
reason intelligently has fueled the current publicity of A.!,
press
which
area
the
is
indisputable
."act
vision.
Despite
that there ?re an increasing number of practical
business
applications of Expert Systems in use today.
When one stops to look at reality it turns out that A.I.
been
used
management:
call
develop
to
two
types
Expert Systems and a
systems
of
variation
of
technology has
particular
of
Expert
Systeins
interest
that we
to
will
Expert Support Systems.
Expert System s
Expert
:-y
stems
can
be
used
increase
to
a
available knowledge that is in limited supply.
the
This
captured
experience
example,
the
encoded
drd
is
then
relevant
experience
available as
a
experts
and
world (16).
ii:.-.ke
Tie
characteristics of
of oil
it
available
program
a
in that region.
well
takes
to
oil
and maKes
at-'lity
to
exploit
They do this by building on
of
an
resource
to
Schlumberger Corpoiation uses its
the interpretive abilities of a handful
human's
expert
the
in
'ess
field.
the
expert.
'Dipmeter Advisor'
to
For
access
of their most productive geological
their
well
field
log
geologists
data
about
all
the
over
the
geological
inferences about the probable location
-3-
Another example of an early system in
Developed
Equipment
Digital
at
Corporation
XCON
University,
Carnegie-Mellon
practical
uses
some
use
in
joint
a
3300
known
is
rules
as
XCON.
effort
with
product
5500
and
descriptions to configure the specific detailed components of VAX and other
computer systems
response to the customers'
in
first determines what,
to
order
the
produces
that
so
it
number of
a
complete
is
diagrams
and
showing
layout for the 50 to 150 components in
application
This
programming
traditional
The
techniques
substantial,
time,
but
time,
an
also
in
not only
ensuring
occurrence
that
a
terms
that
the
system
four years
over
is
customer's
was
and
room
missing
at
acceptance
of
using
initiated.
savings
the
editor's
technical
the
times
several
effort
A.I.
component
no
delays
the
of
this
system (4).
typical
for
then
connections and
unsuccessfully
now
in
and
electrical
before
system has been in daily use
have been
consistent
the
attempted
was
The system
orders.
substitutions and additions have to be made
any,
if
overall
scarce
installation
the
system
(12).
Expert Support Systems
E.S.S.
(Expert Support Systems)
much
wider class
They
do
this
by
of
problems
pairing
the
than
take E.S.
is
human with
techniques and apply them to
possible
the
with
expert
pure
expert system,
thus
a
systems.
creating
a
joint decision process in which the human is the dominant partner, providing
problem-solving
overall
incorporated
in
the
direction
system.
beforehand and made explicit,
However,
level
of
Some
thus
as
well
of
this
as
knowledge
can
to
be
be
thought
not
of
becoming embedded in the expert system.
much of the knowledge may be imprecise and will
consciousness,
knowledge
specific
recalled
to
the
remain below the
conscious
level
of
the
-4-
decision-maker only when
triggered
evolving
the
c.v
problem
context.
systems represent the next generation of Decision Support Systems
(See
Such
(D.S.S.).
for a discussion of Decision Support Systems.)
11
Systems
Expert
knowledge-based
caVied
also
are
systems
They
.
incorporate not only data but the expert knowledge that represents how that
'decent progress
data is to be interpreted and used,
Systems
has
greatly aided by
been
One
factors.
tv/o
machines arc on
the market
with
which
heurist"'cs
knowledge
base
suggestion (See
A
that
today
are
much
system
making
prices
of
A.I.
supporting
facilities
These
environments
reprobing
and
for
the
of
dealing
relevant
worthy
alternative
an
enormous
so-called "LISP"
The
are well -suited
together
weaves
apuli cations,
symbol
permit
significantly
of
such
programming
modify as required and have resulted in
—
and
probing
development of
the
is
capable
development.
low
the
of
).
factor
second
feasible
1
involve
the
as
at
been
has
increase in the computer power avail dble per dollar.
field of Expert
the
in
one
for
tools
manipulation
to
potential
for
Systems,
nonspecial ists
and
incremental
experiment
prototype,
"Power Tools
cj'^eater
Expert
as
Programmers"
than
those
and
(14)
usually
provided by traditional data processiru resources.
Definitions
With tnese examples in mind we can now define Expert Systems as follows:
Expert
Systems
—
computer
programs
that
use
specialized
symbolic
reasoning to solve difficult oroblems well.
In
other words
particular
Expert
problem
configuration)
rather
area
Systems:
(such
(1)
as
than just general
use
specialized
geological
knowledge
analysis
purpose knowledge
or
about
a
computer
that would
apply
5-
to
all
problems,
use
(2)
symbolic
calculations,
than just numerical
and
reasoning rather
often qualitative)
(and
(3)
perform at
of competence
level
a
that is better than that of non-expert humans.
Systems
Support
Expert
use
these
all
same
techniques
but
focus
on
helping people solve the problems:
Support
Expert
Systems
--
computer
programs
that
specialized
use
symbolic reasoning to help people solve difficult problems well.
Heuristic Reasoning
One
of
the
most
important
ways
in
which
expert
systems
in
their
use
heuristic
differ
from
traditional
computer applications
Traditional
applications are completely understood and therefore can employ
calculated
heuristic
of
reasoning.
precise rules that, when followed, lead to the correct
algorithms, that is,
conclusion.
is
For example,
according
techniques.
to
An
the amount of a
a
precise
heuristic
set
payroll
of
check
for an employee
Expert
rules.
Systems
judgemental
system involves
use
reasoning,
and error and therefore is appropriate for more complex problems.
trial
is
The
heuristic decision rules or inference procedures generally provide a good --
but not necessarily optimum -- answer.
Problems appropriate for A.I.
algorithmically;
large,
such as
that
the
is,
by
techniques are those that cannot be solved
precise
rules.
The
problems
are
either
possibilities encountered in the game of chess,
or
too
too
imprecise, such as the diagnosis of a particular person's medical condition.
Components of Expert Systems
To
begin
different
to
from
see
how
expert
traditional
computer
understand what the components of
1).
In
systems
a
(and
expert
applications,
typical
expert
support
it
is
systems)
important
system are
addition to the user interface for communicating with
a
(See
are
to
Figure
human user.
-6-
a
typical
export system also
related to the problem and
using
the
information
these
two
components
problem
added
cliangc-s
to
the
in
has
an
(2)
the
makes
(1)
a
knowledge
to
easier
to
much
or becomes better understood.
Icnowledge
base,
one
v-icts
by
one,
in
solve problems.
rules
and
Systeiiis
change
the
Separating
system
as
For example, new rules can
such
facts and reasoning methods can still be used.
Expert
of
inference engine or reasom'r.g methods for
knowledge base
it
base
Architecture
a
way
tl^ec
all
the
•:he
b'^
old
-7-
applications.
Processing (D.P.)
Data
the
with
work
They
analysts
of
'experts'
and draw out the relevant expertise in a form that can be encoded
in
Three of the most important techniques for encoding
computer program.
a
this knowledge are:
(1)
Production Rules
.
production rules, (2) semantic nets, and (3) frames.
Production rules are particularly useful
systems based on heuristic methods
are
that
condition:
often
or
used
to
represent
action
the
These are
(17).
that should
be
taken
in
a
given
building
"if-then"
consequences
empirical
the
simple
in
of
rules
given
a
situation.
For
convenient
than
example, a medical diagnosis system might have a rule like
If 1)
2)
The patient has fever, and
The patient has
a
runny nose
Then: it is yery likely (.9) that
the patient has a cold.
A computer configuration system might have a rule like
If 1)
2)
There is an unassigned single port disk drive, and
There is a free controller.
Then: Assign the disk drive to the controller port.
Semantic
Nets
Another
.
for
called
networks"
apply
the
that
often
is
representing certain kinds of
production rules
"semantic
formalism
or
"semantic
rule about assigning disk
nets."
drives that
relational
For
-was
more
example,
shown
knowledge
is
order
to
in
above,
a
system
would need to know what part numbers coresponded to single port disk drives,
controllers, and so forth.
Figure
"nodes"
2
shows
how
connected by
this
knowledge
"links"
subsets of other classes.
might
be
represented
that signify which classes
in
a
network
of components
of
are
-8-
SEMANTIC NETWORKS
COMPONENTS
/ELeCTRICAL
DISK DRIVES
/^CONTROLLERS
DUAL -PORT
SINGLE- PORT
DISK DRIVES
'D0-I57t
-)
MECHANICAL
COMPONENTS
COWP'JNENTS
I
DISX DRIVES
D0-I57I-35
(
J
0D-;59l-32
Figure
Frames
number
of
Figure
3
different
shows
requirements)
different
as
how
that
cases,
kinds
it
of
is
convenient
infoniiation
to
about
gather
an
object.
several
dimensions
isuch
describe
electrical
components might
as
into
one
be
a
example,
For
width,
length,
place
and
power
represented
as
components.
Unlike
records in a data base, frames often contain additional
features
"slots"
traditional
such
many
In
.
"default
in
a
values"
"frame"
and
about
"attached
electrical
procedures."
For
example,
if
the
-9-
default
value
for
voltage
requirement
of
volts then the system would infer that a
110
volts
attached
unless
explicit
procedure
might
information
automatically
to
Electrical
Component
electrical
new electrical
the
update
"length," "height," or "width" are changed.
Frames
an
contrary
the
component
is
110
component required
was
"volume"
provided.
slot,
An
whenever
10-
forward chaining and backward chaining
production rulei are
instance, that we have
4
for
a
expert
planning
Imagine
system.
know the current c'lient's tax bracket is 50%,
his liquidity
$100,000, and he has a high tolerance Tor risk.
gas
investments should be recommended for this client.
the
greater than
is
system could infer that exploratory oil
rules,
time,
that we
also
By forward chsiivlng through
the
one at a
With
base, many other investment recommendations might be deduced
a
and
larger rule
well.
a'^
Now imagine tiiat we only want to know that whether explcrsiory oil
investments
gas
interested
any
in
appropriate
are
for
set of production rules like those 3nov,n in Figure
a
financial
personal
Imagine,
.
for
investments
other
a
at
particular
the
client
moment.
av]
The
and
\^e
are
not
sy^^tem
can
use
exactly the same rule base to answer this specific question more efficiently
by
When backward chaining the system
"backward chaining" through the rules.
starts with a goal
gas
investments")
"show that this client needs exploratory oil
(e.g.,
and
asks
reach to achieve this goal.
at
each
stage
what
ana gas
investments,
rule if we know thet risk tolerance is high
a
tax
shelter
indicated we ha^e
to
indicated.
is
find
another
To
rule
conclude
(in
goal
is
acr.'eved:
investments to
t'^'S
we
know
we
can
we can
ose
need
the
(which we already do
this
then check whethe^" its conditions are satisfied.
our
wculd
it
to
For instance, in this example, to conclude that
the client needs exploratory oil
that
subgoals
and
that
case,
In
recommend
a
the
know)
shelter
tax
first
this case,
exploratory
third
one)
tiiey
oil
are,
and
and
is
and
so
gas
client.
With these bdsiu concepts in
.Tiind
we turn now to
Expert Systems and Lxpert Support Systems into
a
a
framework
management conC'^xt.
that puts
11-
Forward Chaining
If
50%
Tax bracket
liquidity
and
is greater than $100,000'
Then
A tax shelter is indicated,
If
A tax shelter is indicated
and risk tolerance is lov/
Then
Recommend developmental oil
and gas investments.
If
A tax shelter is indicated
and risk tolerance is high
Then
Recommend exploratory oij
and gas investments.
Bac kwa rd Cha i ni ng
(Subgoaling)
What about exploratory oil and gas?
If
<r
Tax bracket = 50%
and liquidity is greater than $100,000
Then
A tax shelter is indicated.
If
A tax shelter is indicated
and risk tolerance is low
Then
Recommend developmental oil
and gas investments.
If
A tax shelter is indicated
and risk tolerance is high
Then
Recommend exploratory oil
and gas investments.
Figure 4
12-
FRAMEWORK FOR EXPERT SUPPORT SYSTEMS
The
framework developed in
this
begins
sect"! on
to
allov.
us
identify
to
those classes of business problems that are appropriate for Data Processing
(D.P.), Decision Support Systems (D.S.S.),
Expert Systems (F.5.), and Expert
Support
addition,
Systems
(E.S.S.).
We
can,
in
clarify
relative
the
contributions of humans and computers in fie various classes of applications.
This
framework
"Framework
(15)
to
Scott
s
Morton
earlier
Information
Anthony's
control
the
work
(2).
argued
strategic
Figure
that
of
Gorry
Systems, "(8)
work on structured vs.
seminal
Robert
operational
and
Management
of
Herbert Simon'
extends
in
wfiich
planning,
improva
to
the
they
relate
unstructured decision making
managei.pnt
presents this original
5
Scott/Morton,
and
qualify
control,
Gorry
rramework.
of
and
decisions,
the
manager must seek not only to match the type and qu^ility of information and
its
presentation to the category of decision,
but
also
to
choose
a
that reflects ihe degree of the problem's structure.
Strategic
Planning
Structured
A
inanagement
Control
>
<e
Semi-Structurad
Unstructured
Operational
Control
V
Figure
5
system
-13-
With
benefit of experience
the
building
1n
Decision
using
and
Support
Systems, and in light of the insights garnered from the field of Artificial
it is useful
Intelligence,
dimension of the original
in
the original
framework.
Intelligence,
three phases;
into
to expand and rethink the
article that
Simon had broken down decision making
Design and Choice
maker.
As
result they
a
argued
It was
{I,D,C).
structured decision was one where all
a
{I,D,C) were fully understood and
phases
structured/unstructured
could be
three
"computable" by the human decision
programmed.
unstructured decisions,
In
one or more of these three phases was not fully understood.
We
can
Design
and
extend
distinction
this
Choice with
Alan
Newell
replacing
by
insightful
's
Simon's,
Intelligence,
categorization
of
problem
solving (13), as consisting of the following components:
Goals; Constraints; State Space; Search Control
In
a
business
context,
it
seems
helpful
Knowledge; and Operators.
to
relabel
these
problem
characteristics and group them into four categories:
1.
Data
-
the dimensions and values necessary to represent the state of
the world that is relevant to the problem (i.e., the "state space")
2.
Procedures
the sequences of steps (or "operators") used in solving
-
the problem.
3.
Goals and Constraints
-
the
desired results of problem solving and
the constraints on what can and cannot be done
4.
Strategies
-
procedures
to
flexible
the
apply
to
strategies
achieve
goals
used
(i.e.
deciding
in
the
"search
which
control
knowledge")
Thus
original
we
argue
that
the
sructured
-
unstructured
continuum
framework can be thought of using these four elements.
of
the
A problem
-14-
is
fully
structured when
elements
four
all
are
well
and
understood
Such a categorization helps
unstructured when the four remain vague.
illustrated in Figure
match classes of --ystem with types of problem, as
PROBLEM TYPES
I
D
P.
11
ni
IV
D. S.S.
E.S.
E.SS.
Da*a
PrGC»»dure8
T
Goals 6
Constraints
!l
Fiisxible
Strataqies
lU
Key
Done by
Computer
Done by
Peop le
Figure
6
fully
us
6.
to
-15-
For some problems we can apply a standard procedure
•
and proceed directly to a conclusion with
or formula)
an algorithm
(i.e.,
need for flexible
no
For example, we can use standard procedures
problem-solving strategies.
to
compute withholding taxes and prepare employee paychecks and we can use the
classical economic order quantity formula to solve straightforward inventory
problems.
control
other
In
cases
solution
a
can
identifying alternative approaches, and thinking through
simulation)
effects
the
of
found
be
(in
only
by
some cases
via
alternative courses of action.
these
chooses the approach that appears to create the best result.
to
determine which of three
manager
this
to
explore
the
sales force utilization,
expenses,
of
want
might
section
we
discuss
will
strategies
sales
for
use
to
consequences
revenue, and so forth.
range
the
for
In"
product,
a
advertising
the remainder
different
these
of
then
For example,
new
a
each
of
One
types
of
problems and the appropriate kinds of systems for each.
Type
I
Problems
-
Data Processing
A fully structured problem is one in which all
the problem are structured.
That is, we have well
four of the elements of
stated goals, and we can
specify the input data needed, and there are standard procedures by which a
solution
may
evaluating
calculated.
be
alternatives
No
are
complex
strategies
Fully
needed.
for
structured
generating
and
problems
are
computable and one can decide if such computation is justifiable given the
amounts of time and computing resource involved.
These
problems
techniques.
problem
is
programmer)
In
well
has
are well
conventional
defined.
already
suited
to
the
virtually
programming,
In
effect,
solved the
of conventional
use
the
problem.
expert
He
or
programming
everything
(i.e.,
she
about
the
must only
the
analyst/
sequence
-16-
the
data
the
through
decision
of
class
problems
problems,
problems
can
thai:
programming techniques.
conventional
Figure
particular program.
the
eccnomically
using
refer to this class as Type
We will
thought
historically
solved
be
pictorially
represents
6
of
suited
ones
as
for
I
Data
Processing.
It is
programming
interesting to note that the economics of conventional
tne provision
are being fundamentally altered with
"analyst's workbench."
These
(14)
professional
are
such
new tools
of
stations
work
as
an
used
by
the systems analyst to develop flow chart representations of the problem and
automatically
move
then
happen
stations
these
to
to
testable,
code.
techniques,
A.I.
use
running
more
advanced
turning
these
Tht
thus
of
new
techniques into tools to make our old approaches more effective in classical
D.P.
application areas.
Type
II
Problems
As we
Decision Support Systems
-
leave problems which are
fully
structured we
begin
to
where
where
themselves,
goals
procedures
standard
the
data
constraints
and
processing
systems
possibility
in
are
may
partially
only
cases,
these
nSt
intuition,
and
the
control
problem
and general
solving
may
use
their
solving strategies.
knowledge
of
and where
the
Fortunately,
problems.
we
computer
the
have
perform
data
the
the
humans to use
knowledge to formulate problems, modify
and
interpret
all
these
results.
the
shows, the human users may provide or modify data,
they
by
Traditional
well -understood parts of the problem solving while relying on
their goals,
sufficient
represented,
understoou.
letting
of
but
helpful
incompletely
be
cannot solve
these
are
with
These are
many of the problems organizations have to grapple with each day.
cases
deal
As
Figure
procedures or goals,
factors
to
decide
on
6
and
problem-
-17-
many of the best known Decision Support Systems (11) for example, the
In
standard procedures to certain highly
computer applies
relies on
and
situation
given
example,
(11)
human users
the
decide which procedures
to
whether
a
result
given
structured data but
appropriate
are
satisfactory
is
or
computer for either making
the
For
not.
deciding
decisions about
final
procedures
which
portfolio
composition
analysis.
They used the computer to execute the procedures they
for
or
appropriate,
for
example
calculating
returns,
the
managers
themselves
but
whether
flexible
on
portfolio
proposed
diversification
given
a
spreadsheet programs
people who use
similar
a
investment managers who used the portfolio management system
the
did not rely on
decided
in
strategy
of
proposing
was
"what if"
for
an
alternative
return
or
today
diversity
action,
for
use
to
felt were
expected
and
portfolios
acceptable.
analyses
letting
and
Many
follow
a
computer
the
predict its consequences and then deciding what action to propose next.
Type III
-
Using
expert
Expert Systems
programming
A.I.
systems
are
able
techniques
to
encode
like
some
production
of
the
same
rules
and
kinds
of
frames,
goals,
heuristics, and strategies that people use in solving problems but that have
previously
been
techniques
make
very
it
standard procedures,
difficult
possible
to
to
use
design
but instead use
in
computer
systems
that
programs.
don't
just
These
follow
flexible problem-solving strategies to
explore a number of possible alternatives before picking a solution.
For
some
cases,
like
the
XCON
system,
these
almost all the relevant knowledge about the problem.
techniques
can
As of 1983,
capture
fewer than
one out of every 1000 orders configured by XCON was misconfigured because of
missing
or
incorrect
rules.
(Only
about
10%
of
the
orders
had
to
be
-18-
and almost all
corrected for any reason at all
missing descriptions of rarely
We
the
call
to
parts (4).)
i-is-jd
problems where
of these errors were due
essentially
the
all
relevant knowledge
flexible problem solving can be o.icoded Type III Problems.
for
The systems that
solve them are Expert Systems.
It
instructive
is
probably
most
the
however,
note,
to
extensively
tested
system
with
even
that
commercial
in
knowledge is continually being cdded and humans still
system configures.
which
XCON,
new
today,
use
is
check every order the
As the developers of XCON '^emark:
"There is no more -eason to believe now than
there was
[in
1979]
that [XCON]
has
all
the
configuration
knowledge
relevant
to
its
task.
This, coupled with the f.ict that [XCON] deals with
an
ever-changing dor-aio implies its Development
will never be finished."
(See 4, page 27)
If
which operates
XCON,
configuration,
appear*;
much
in
fairly
the
never contains -iH
less
likely
that
restricted domain
computer order
of
the knowledge relevant to its problem,
<^e
will
ever
be
able
to
codify
strategic planning, and project management.
the
fairly
simple
continually
machines,
case
changing
and
parts
manufacturing process.
What this
importance
in
of
and
are
job
shop
possibly
needed
and
analysis,
Even in what might appear to be
scheduling,
implicit
the
all
knowledge needed for less clearly bounded problems like financial
it
rhere
are
constraints
available
for'
often
on
different
very
many
people,
what
steps
in
a
(See 7.;
suggests is
that for very many of
business we
should
focus
tiie
our uttent'-on
that support expert users rather than replacing them.
problems
on
of
practical
designing
systems
-19-
Even
in
Expert Support Systems
-
Type IV
situations where
in
of
four areas
all
possible
capabilities
systems
computers
of
of
problem-solving knowledge,
problem cannot feasibly be encoded,
the
expert
use
to
important kinds
technologies
previous
beyond
dramatically
This
techniques.
it
such
still
is
extends
as
the
and
D.P.
D.S.S.
What is important, in these cases,
(See
Figure
enable their human users
process.
other
In
accessible
and
problem-solving
capabilities.
good
very
with
6)
to
words,
malleable
and
For example,
to
Expert Support Systems
design
embedded
deeply
a
good
Many
.
expert
support
expert
to
support
users
the MYCIN medical
the
system
problem-solving
should
both
be
make
systems
their
explanation
providing
by
that
interfaces"
"user
easily inspect and control
accessible
process
is
diagnosis program can explain
to a doctor at any time why it is asking for a given piece of information or
what rules
it
used
arrive
to
at
a
conclusion.
given
For
system
a
to
be
malleable, users should be able to easily change data, procedures, goals, or
problem-solving process.
strategies at any important point in the
with
this capability
are still
but an early
rare,
Advisor suggests how it might be provided (5).
satisfactory
way
to
automatically
detect
patterns, so human experts used a graphical
annotate these patterns.
In
version
of
the
Systems
Dipmeter
this version there was no
certain
kinds
of
geological
display of the data to mark and
The system then continued its analysis using this
information.
An
even more
vivid example of how
a
system can be made accessible
and
malleable is provided by the Steamer Program (See 10) for teaching people to
reason
about
operating
a
steam
plant.
This
system
has
colorful
graphic
-20-
di
splays
schematic
the
of
flows
different valves and gauges,
of
device)
control
to
continually updates
these
plant,
(using
and
simulation rasults and expert diagnostics
its
system
The
forth.
so
Users
pointing
"mouse"
a
of
status
the
different places.
in
displays
temperatures,
valves,
the
simulated
pressures
the
an.i
manipulate
can
system
the
the
in
based on
these usf.r actions.
Summary of Framev/ork
This framework helps clarify
as
did
matching
Gorry
original
the
system
type
to
number of
a
Scott
and
problem
Morton
type.
framework,
the
In
it highlights,
First,
is«;iies.
importance
the
article,
1971
o.-iginal
however, the primary practical
pc'nts to be made were that traditional
technologies
usid
should
probleiri wnere
not
be
new D.S.S.
semi -structured
for
D.P.
unstructured
and
technologies were more app^-opriate;
of
secondly that
interactive human/computer use opened up an extended class of problems where
The most
computars could be usefully exploited.
to
be
today
ifiade
should
not be
again
is
used
for
first,
two-foid:
partially
point
important practical
that
"pure"
expert
systems
understood problems where expert support
systems are more appropriate, and second that expert systems techniques can
be
used
dramatically
to
extend
capabilities
the
decision
traditional
or
supoort systems.
Figure
shows,
7
in an
admittedly simplified way,
support systems as the next logical
progressions.
On the left side of the
ouc of
a
beings
so:ve complex
the
practical
figure
step
in
each
figure, we
of
reflects
a
largely
in
actual
two
developed
for helping real
organizations.
independent
somewhat separate
see that D.S.S.
recognition of the limits of D.P.
problems
hew we can view expert
evolution
The
that
right
took
human
side
of
place
in
-21-
computer science research laboratories and that developed from
of
the
limits
of
traditional
computer
science
techniques
kinds of complex problems that people are able to solve.
point where
these
two
Progressions in Computer System Development
Figure
7
for
recognition
solving
the
We are now at the
separate progressions can be united
broad range of important practical problems.
a
to
help
solve
a
-22-
THE IMPORTANCE OF EXPERT SUPPORT SYSTEMS FOR MANAGEMENT
The
real
harness
and
importance of E.S.S.
make
use
full
lies
our
of
experience of key members
of
benefits
expert's
capturing
in
the
ability
the
in
scarcest
resource:
the
There
be
organization.
the
experience
these
of
can
making
and
systems
to
and
talent
considerable
available
it
to
those in an organization that ar^ less expert in the subject in question.
As organizations and their problems become more complex,
benefit
from
initiating
prototype
and
E.S.
management can
The
E.S.S. 's.
question
now
is
for
facing managers is when to start, and in which areas.
The
'when'
exploratory
education
start
to
For
work.
active
and
relatively
is
oi'ice
monitoring
the exploration
is
to
organizations
some
investment may take the form of
few,
easy
of
the
this
field.
experimental
a,i
ove*',
^inswer.
It
will
be
a
others
For
forward with a full-fledged working prototype.
program
the
of
initial
lew budget prototype.
make good economic
it will
'now'
For a
sense
to
go
and technological
Conceptual
developments have made it possible to begin an active prototype development
phase.
These developments have ta<en place in several areas, for example:
Hardware
is
getting
languages
such
addition,
the concepts,
--
:he
expert
work
—
as
LISP
involved
cheaper,
smaller,
(18)
more
enable us to deai
tools,
in
and
and
canturing
Programming
powerful.
with
A.I.
concepts.
technique3 for knowledge engineering
and
codifying
are beginning to oe understood.
A.I.
the
knowledge
of
cost
of
hardware
becomes
irrelevant
to
an
research has always been
characterized by its need for large amounts of computing resources.
the
In
the
economics
of
As
problem
solution, the techniques of A.I. are becoming more economically viable.
•23-
As companies
the
broad
collection
begin to
install
or
narrow
and
interpretation
development will
band
possibilities
varieties,
of
of either
communications networks
global
data.
for
the
organizations,
some
In
abound
this
for enhanced decision making
provide the potential
and
the opportunity for effective use of A.I. techniques.
The recent proliferation of firms offering specialized A.I.
services has
resulted in the creation of new software and an increasingly large group
of knowledge engineers.
Some have started companies and are hiring and
training people who are focussing on business applications.
The second question facing managers is the one of where
area
possible
for
organization's assets.
be
essential
experimentation
initial
what looks to be
In
acquire
to
and
is
productive
the
astutely.
One
start.
to
of
use
decade of low growth,
a
assets
use
(See 3.)
an
it will
Equipment
Digital
Corporation's use of an Expert System for "equipment configuration control"
is one example.
A second sensible place in which to begin using A.I.
is
in
those areas in which the organization stands to gain a distinct competitive
Schlumberger
advantage.
would
seem
to
feel
their
that
used
E.S.
as
a
drilling advisor is one such example.
It
is
interesting
that
of
the
more
than
20
organizations
known to the authors to be investing in work in E.S.
and E.S.S.
would
given
allow
themselves
to
be
quoted.
The
reasons
personally
almost none
basically
boiled
down to the fact that they were experimenting with prototypes that they were
expecting to give them
product or service.
are
cases such as an
a
competitive advantage in making or delivering their
Examples of this where we can quote without attribution
E.S.S.
for
supporting the cross
selling
of
financial
services products, such as an insurance salesman selling a tax shelter.
In
24-
another
case
it
dssire
the
is
of
evaluate the credit worthiness of
It
somewhat
that
primitive
there
are
ability
provide
help we can
any
with
deal
permit
can
E.S.
the
building
of
the situation is even brighter
With E.S.S.
beleaguered
the
to
problem areas where even our
great many
a
to
organization
services
financial
loan applicant.
a
first generation systems.
useful
as
clear
is
a
'expert'
leverage
provide
will
for the organization.
The Problems, Risks and Issues
It would be irresponsible to conclude this article without commenting on
the
Expert
that
fact
infancy,
researchers
and
users
and
capabilities of these new systems.
expert
systems
will
poorly
be
backlash will hinder progress.
Support
Expert
and
Systems
must
alike
One risk,
defined
It can
and
Systems
are
about
realistic
be
already apparent,
oversold,
and
their
in
that the
is
the
the
resulting
be argued that the Western economies
lost the most recent round on the economic battlefield to Japan, due in part
to
failure
their
to
manage
productivity
inability to select the markets
similar
with
risk
careless we will
Expert
lose
out
in
Systems
in
and
quality
well
as
which they wished to excel.
and
their
exploiting
this
face
We
a
if
we
are
potential
of
the
applications,
particular
their
as
and
information era.
There
is
a
paying careful
danger
system that
this
same
proceeding
too
quickly,
attention to what we are doing.
well embed our knowledge
a
of
is
system
recklessly,
One example
is
without
that we may
(necessarily incomplete at any moment in time) into
effective when
is
too
used
by
used
by
others,
the
person who
however,
there
created
is
it.
risk
a
misapplication; holes in another user's knowledge could represent
a
When
of
pivotal
-25-
element
in
implicitly
leading
logic
the
recognized
by
the
to
While
solution.
a
creator of
knowledge
the
base,
are
holes
these
they
may
be
be
met
if
quite invisible to a new user of the knowledge base.
challenge
The
managers
Expert
treat
Support
which will
of
the
proceeding
subject
Systems,
and
of
at
an
appropriate
Intelligence,
Artificial
Decision
require management attention
Support
if
it
Systems
is
to
Managers must recognize the differences between Type
which
the
older techniques
for Types III and IV.
are
pace
appropriate,
and
the
be
I
as
can
Expert
a
Systems,
serious
topic
exploited properly.
and
II
problems,
new methods
for
available
26-
CONCLUSIONS
There are, then,
However,
for some time.
proceed
we
if
some basic risks and constraints which will
potential
the
and
techniques are obvious,
of A.I.
cautiously,' acknowledging
be with us
problems,
the
we
begin
can
to
achieve worthwhile results.
illustrations
The
here
used
merely
are
that have been described in some detail
in
relatively
a
start-up
phase
Business
has
and,
The
a
period
Expert
for
attempted
despite
developing
brief
Systems
develop
to
enormity
the
of
some
(see References)
primitive
time
with
and
Expert
expert
some
of
of
two
of
twenty
or
and have been built
tools.
This
Support Systems,
Phase
systems
the
fifteen
applications
problems,
has
is
a
Zero.
1980
since
succeeded
in
number of simple and powerful prototypes.
state
of
art
the
such
is
that everyone
building an
expert
system
must endure this primitive start-up phase in order to learn what is involved
in this fascinating new field.
for
E.S.
and
E.S.S.
to
be
We expect that it will
recognized
fully
as
take until
about 1990
achieved
worthwhile
having
Dusiness results.
However
albeit
in
Expert
a
Systems
primitive
these tools to increase
the
Expert
The
form.
value for its stakeholders.
a
and
Support
challenge
are
management
with
is
to
us
now,
harness
effectiveness of the organization and thus add
The pioneering firms are leading the way;
section of territory has been staked out,
leaders will be hard to equal.
now.
for
Systems
the
experience gained by
once
these
The time to examine the options carefully is
-27-
REFERENCES
1.
"The Next Revolution in Computer Programming,"
Alexander, T.
Fortune October 29, 1984, pp. 81-86.
,
,
2.
Anthony, R.N., "Planning and Control Systems: A Framework
Harvard
Graduate School
University
Boston:
Analysis,"
Business Administration, 1965.
3.
Business Week , "Artificial
age begins," March 3, 1982.
4.
Bachant, J., and McDermott, J., "Rl Revisited: Four years
the Trenches," AI Magazine, Fall, 1984. pp. 21-32.
5.
Davis R. , Austin, H. , Carl born, I., Frawley, B., Pruchnik, P.,
Dipmeter
Advisor:
Sneiderman,
Gilreath,
J. A.,
"The
R. ,
Proceedings of the
Interpretation of Geological
Signals,"
Seventh
International
Joint
Con^S'^snce
Artificial
o"
Intelligence Vancouver, Canada: 1981, pp. 846-849.
Intelligence:
The
for
of
second computer
in
,
6.
Fortune , "Teaching Computers the Art of Reason," May 17, 1982,
and "Computers on the Road to Self- Improvement," June 14, 1982.
7.
"Constraint-directed Search:
A
Case
Study
of
Fox,
M.S.,
Carnegie-Mellon
University
Robotics
Job-Shop
Scheduling,"
Institute, Technical
Report No CMU-RI-TR-83-22, Pittsburgh,
Pennsylvania: 1983.
8.
Gorry, Anthony, and Michael S. Scott Morton, "A Framework for
Information
Management Review,
Management
Systems,"
Sloan
Massachusetts Institute of Technology, Vol. 13, No. 1, Fall
1971.
9.
Hayes-Roth, Frederick, Donald A. Waterman, Douglas B.
Editors, Building Expert Systems Addi son-Wesley, 1983,
Lenat,
,
10.
Hollan, J.D., Hutchins, E.L. & Weitzman,
Interactive,
Inspectable Simulation-based
AI Magazine, Sumner, 1984, pp. 15-28.
11.
Keen, Peter, and Michael S. Scott Morton, Decision Support
Systems:
Perspective ,
Reading,
An
Organizational
Massachusetts: Addi son-Wesley Publishing Company, Inc., 1978.
12.
McDermott, John, "Rl
A Rule-Based Configurer of Computer
Systems," Artificial Intelligence, Vol. 19, No. 1, 1982.
:
"Steamer: An
L.,
Training System,"
-28-
13.
Problem
and
decision
solving,
Newell,
"Reasoning:
A.,
In R.
fundamental
category,"
processes: The Problem space as a
Performance
Hillsdale,
and
VIII
Nickerson (Ed.) Attention
N.J.: Erlbdum, 1 9Sn:
,
14.
15.
Programmers,"
Sheil,
"Power
Tools
B.
February, 1983, pp. 131-144.
for
Simon, Herbert A., The
Harper & Row, ISFII
Science
New
of
Datamation
Management
Decision
,
,
N.Y.
16.
Artificial
Winston,
Patrick Henry,
Co., Inc., 1984,
Addi son-Wesley Publ
.
88, 132-134.
17.
Winston, supra
18.
Hayes-Roth, supra, Chs.
,
p.
5,
K& ti.M
tii^\
8
25^
CU
6,
9.
Intelligence ,
ly//.
2nd
Ed.,
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