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NAN(NA
August
1982
Report
No.
STAN-CS-82-9S6
Also
numbered
HIPP-82-29
Artificial
Intelligence:
Cognition
as
Computation
by
Avron
Barr
M
Department
of
Computer
Science
jAgord
University
gtanford,
CA
94305
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NO.
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JUN
S2
--
2
OF
- -
"August
1982
Artificial
Intelligence:
Cognition
as
Computation'
Avron
Barr
Ii.
The
ability
and
compulsion
to
know
are
as
characteristic
of
our
human
nature
as
are
our
physical
posture
and
our
languages.
Knowledge
and
intelligence,
as
scientific
concepts,
arc
used
to
describe
how
an
organism's
experience
appears
to
mediate
its
behavior.
This
report
discusses
the
relation
between
artificial
intelligence
(AI)
research
in
computer
science
and
the
approaches
of
other
disciplines
that
study
the
nature
of
intelligence.
cognition,
and
mind. The
sutte
of
Al
after
25
years
of
work
in
the
field
is
reviewed.
as
arc
the
views
of
its
practitioners
about
its relation
to
cognate
disciplines.
i'he
report
concludes
with
a
discussion
of
some
possible
effects
on
our
scientific
work
of
emerging
commercial
applications
of
Al
technology,
that
is,
machines
that
can
know
and
can
take
part
in
human
cognitive activities.
Artificial
Intelligence
Artificial
intelligence
is
the
part
of
computer
science
concerned
with
creating
and
studying
computer
programs
that
exhibit
behavioral
characteristics
we
identify
as
intelligent
in
human
behavior-knowing.
reasoning,
learning, problem
solving,
language
understanding,
and
so
on.
Since
the
field's
emergence
in
the
mid-1950s.
AI
researchers
have
developed
dozens
of
programs
and
programming
techniques that
support
some
sort
of
"intelligent"
behavior.
Although
there
are
many
attitudes
expressed
by
researchers
in
the
field,
most
of
these
people
are
motivated
in
their
work
on
intelligent
computer
programs
by
the
thought
that
this
work
may
lead
to
a
new
understanding
of
mind:
Al
has
also
embraced
the
larger
scientific
goal
of
constructing
an
information-processing
theory
of
intelligence.
If
such
a
science
of
intelligence
could
be
developed,
it
could
guide
tie
design
of
intelligent
machines
as
well
as
explicate
intelligent
behavior
as
it
occurs
in
humans
and other
animals. (Nilsson.
1980.
p. 2)
lTo
appear
in
7he
Study
of
Information:
Interdisciplinary
Messages
edited
by
Fritz
Machlup
and
Una
Mansfield.
and
published
by
John
Wiley
and
Sons.
New
York.
1983.
2
Whether
or
not
it
leads
to
a
bctter
understanding
of
the
mind,
there
is
every
evidence
that
current
work
in
Al
will
lead
to
a
new
intelligent
lechnology-
that
may
have
dramatic
effects
on
our
society.
Experimcntal
Al
systems
have
already
generated
interest
and
enthusiasm
in
industry
and
are
being
developed
commerciall
y.
These
experimental
systems
include
programs
that-
*
solve
some
hard
problems
in
chemistry,
biology,
geology,
engineering,
and
medicine
at
human-
expert
levels
of
performance,
*
manipulate
robotic
devices
to
perform
some
useful
sensory-motor
tasks;
and
"
answer
questions
posed
in
restricted dialects
of
E'nglish
(French,
Japanese, etc.).
Useful
AI
programs
will
play
an
important
part
in
the
evolution
of
the
role
of
compLters
in
our
lies-a
role
that
has
changed,
in
our
lifetimes,
from
remote
to
commonplace
and
that,
if
current
expectations
about
computing
cost
and
power
are
correct,
is
likely
to
evolve
further
from
useful
to
essential.
The
Origins
of
Artificial
Intelligence
Scientific
fields
emerge
as
the
concerns
of
scientists congeal
around
various
phenomena.
Sciences
are
not
defined,
they
are
recognized.
(Newell,
1973a,
p. 1)
The
intellectual
currents
of
the
times
help
direct
scientists
to
the
study
of
certain
phenomena.
For
the
esolution
of
Al,
the
two
most
important
forces
in
the
intellectual environment
of
the
1930s
and
1940s
were
maihenatical
logic, which
had
been
under rapid
development
since
the
end
of
the
19th
century,
and
new
ideas
about
computation.
The logical
systems
of
Frege,
Whitehead
and
Russell,
Tarski,
and
others
showed
that
some
aspects
of
reasoning
could
be
formalized
in
a
relatively
simple framework:
The
fundamental
contribution
was
to
demonstrate by
example
that
the
manipulation
of
symbols
(at
least
some
manipulation
of
some
symbols)
could
be
described
in
terms
of
specific,
concrete
processes
quite
as
readily
as
could
the
manipulation
of
pine
boards
in
a
carpenter
shop....
Formal
logic,
if
it
showed
nothing
else,
showed
that
ideas-at
least
some
ideas-could
be
represented
by
symbols, and
that
these
symbols
could
be
altered
in
meaningful
ways
by precisely
defined
processes.
(Newell
and
Simon.
1972,
p.
877)
Mathematical logic continues
to
be
an
active
area
of
investigation
in
Al,
in
part
because
general-purpose,
logico-deductive
systems
have
been
successfully
implemented
on
computers.
But
even
before
the
advent
of
computers,
the
mathematical
formalization
of
logical
reasoning
shaped
people's
conception
of
the
relation
between
computation
and
intelligence.
Ideas
about
the
nature
of
computation,
due
to
Church,
Turing,
and others,
provided
the
link
between
the
notion
of
formalization
of
reasoning and
the
computing
machines
about
to
be
invented. What
was
essential
in
this
work
was
the
abstract
conception
of
computation
as
symbol
processing.
The
first
computers
were
numerical
calculators
that
did
not
appear
to
embody
much
intelligence
at
all.
lut
before
these
machines
were
even
designed,
Church
and
Tiuring
had
seen
that
numbers
were
an
inessecitial
aspect
of
coimput
ation-they
were
just
one
way
of
interpreting
the
internal
states
of
the
machine:
In
their
striving
to
handle
symbols
rigorously
and
objectively-as
objects-logicians
became
more
and
more
explicit
in
describing
the
processing
system
that
was
supposed
to
manipulate
the
symbols.
In
1936,
Alan
Turing,
an
English
logician,
described
the
procssor,
now
known
as
the
Turing
machine,
that
is
regarded
as
the
culmination
of
this
drive
to%%ard
formnali/ation.
(Newell
and Simon,
1972,
p.
878)
'111C
model
of
a
Turing
machine
c*ontains
within
it
the
notions
both
of
what
can
be
computed
and
of
universal
machines-computers
that
can
do
anything
that
can
be
done
by
any
machine.
(Newell
and
Simon,
1976,
p.
117)
Turing.
who
has been
called
the
father
of
Al.
not
only
invented
a
simple,
universal,
and
nonnimerical
model
of
computation
but
also
argued
directly
for
the
possibility
that
computational
mechanisms
could
behave
in
a
way
that
would
be
perceived
as
intelligent:
Thought
was
still
wholly
intangible
and
ineffable
until
modern
fonlal
logic interpreted
it
as
the
manipulation
of
fonnal
tokens.
And
it
seemed
still
to
inhabit
mainly
the
heaven
of
Platonic
ideals,
or
the
eqtally
obscure
spaces
of
the
human
mind,
until
computers
taught
us
how
symbols
could
be
processed
by machines.
A.
M.
Turing
...
made
his
great
contributions
at
the
mid-century
crossroads
of
these
developments that
led
from modern
logic
to
the
computer.
(Newell
and
Simon.
1976,
p.
125)
As
Allen
Newell
and
Herbert
Simon
point
out
in
the
"'Ilistorical
Epilogue"
to
their
classic
work
Hunan
Problem
Solving
(1972),
there
were
other
strong
intellectual
currents
from
several
directions
that converged
in
the
middle
of
this
century
in
the
people
who
founded
the science
of
artificial
intelligence.
'The
concepts
of
cybernetics
and
self-organiiing
systems
of
Wiener,
McCulloch,
and
others
dealt
with
the
macroscopic
behavior
of
"locally
simple"
systems.
'Te
cyberneticians
influenced
many
fields
because
their
thinking
spanned
many
fields,
linking
ideas
about
the
workings
of
the
nervous
system
with
information
theory
and
control
theory,
as
well
as
with
logic
and
computation.
Their
ideas
were
part
of
the
zeitgeist,
but
in
many
cases
the
cyberncticians influenced
early workers
in
Al
more
directly-as
their
teachers.
What eventually
connected
these
diverse
ideas
was,
of
course,
the
development
of
the
computing
machines
themselves,
conceived by
Babbage
and
guided
in
this
century
by
Turing,
von
Neumann,
and
others.
It
was
not
long after
the
machines
became
available
that
people
began
to
try
to
write
programs
to
solve
puzzles,
play
chess,
and
translate
texts
from
one
language
to
another-the
first
A I
programs.
4
What
was
it
about
computers that triggered
the
development
of
Al?
Many
ideas
about
computing
relevant
to
Al
emerged
in
the
early
designs-ideas
about
memories
and
processors,
about
systems
and
control,
and
about
levels
of
languages
and
programs. But the single
attribute
of
tie
new
machines
that
brought
about
the
emergence
of
the
new science
was
their
inherent
potential
for
complexiio, encouraging
(in
several
fields)
the
development
of
new
and
more
direct
ways
of
describing complex
processes-in
ternis
of
complicated
data
structures
and
procedures
with
hundreds
of
different
steps:
Problem
solving
behaviors,
even
in
the
relatively well-structured
task
environments
that
we
have
used
in
our
research,
have
generally
been
regarded
as
highly
complex
forms
of
human
behavior-so
complex
that
for
a
whole
generation
they
were
usually
aNoided
in
the
psychological
laboratory
in favor
of
behaviors
that
seemed
to
be
simple
....
The
appearance
of
the
modern
computer
at
the
end
of
World
War
If
gave
us
and
other
researchers
the
courage
to
return
to
complex
cognitive
performances
as
our
source
of
data
... a
device
capable
of
symbol-
manipulating
behavior
at
levels
of
complexity
and
generality
unprecedented
for
man-made
mechanisms
....
This
was
part
of
the
general
insight
of
cybernetics,
dela
,ed
by
ten
years
and
applied
to
discrete
symbolic behavior
rather
than
to
continLous
feedback
systems.
(Newell
and
Simon,
1972.
pp.
869-870)
Computers,
Complexity, and
Intelligence
As Pamela
McCorduck
notes
in
her
entertaining
historical
study
of
Al
Machines
Who
Think
(1979),
there
has
been
a
longstanding
connection
between
the
idea
of
complex
mechanical
devices and
intelligence.
Starting
with
the
fabulously
intricate
clocks
and
mechanical automata
of
past
centuries,
people
have
made
an
intuitive
link
between
the
complexity
of
a
machine's
operation
and
some
aspects
of
their
own
mental
life.
Over
the last
few
centuries,
new
technologies
have
resulted
in
a
dramatic
increase
in
the
complexity
we
can
achieve
in
the
things
we
build.
Modern
computer
systems
are
more
complex
by
several
orders
of
magnitude
than
anything
humans
have
built
before.
The first
work
on
computers
in
this
century
focused on
the
numerical
computations
that
had
previously
been
performed
collaboratively
by
teams
of
hundreds
of
clerks,
organized
so
that
each
did
one
small
subcalculation.and
passcd
the
results
on
to
the
clerk
at
the
next
desk.
Not
long
after
the
dramatic
success
of
the
first digital
computers
with
these
elaborate
calculations,
people
began
to
explore
the
possibility
of
more
generally
intelligent
mechanical
behavior-could
machines
play
chess,
prove
theorems,
or
translate
languages?
They
could,
but
not
very
well.
The
computer
performs
its
calculations
following
the
step-by-step
instructions
it
is
given-thc
method
must
be
specified
in complete
detail.
Most
computer
scientists
are
concerned
with
designing
new
algorithms,
new languages,
and
new
machines
for
performing
tasks
like
solving
LL
S
equations
and
alphabetizing
lists-tasks
that
people
perform
using methods
they
can
explicate. However.
people
cannot
specify how
they
decide
which
move
to
make
in
a
game
of
chess
or
how
they
determine
that
two
sentences
"mean
the
same
thing."
lhc
realization
that
the
detailed
steps
of
almost
all
intelligent
human
activity
were
unknown marked
the
beginning
of
artificial
intelligence
as
a
separate
part
of
computer
science.
Al
researchers
investigate
different
kinds
of
computation,
and
different
ways
of
describing
computation,
in
an
attempt
not
just
to
create
intelligent
artifacts
but
also
to
understand
what intelligence
is. A
basic
tenet
of
Al
is
that
human
intellectual
capacity
will
best
be
described
in
the same
terms
as
tie
ones
researchers
invent
to
describe
their
programs.
However,
they are
just
beginning
to
learn
enough
about
those
programs
to
know
how
to
describe
them
scientifically-in
terms
of
concepts
that
illuminate
their
nature
and
differentiate
among
fundamental
categories. These
ideas
about computation
have
been
dceeloped
in
programs
that
perform
many
different
tasks,
sometimes
at
the
level
of
human
performance,
often at
a
much
lower
level.
Most
of
these
methods
are
obviously
not
the
same
as
the
ones
that
people
use
to
perform
the
tasks-some
of
them might
be.
The
Status
of
Artificial
Intelligence
Many
intelligent
activities
besides
numerical
calculation
and
information
retrieval
have been
carried
on
by
programs.
Many
key
aspects
of
thought-like
recognizing
people's
faces
and
reasoning
by
analogy-are
still
puzzles;
they
are
performed
so
unconsciously
by
people
that
adequate computational
mechanisms
have
not
been
postulated.
Some
of
the
successes,
as
well
as
some
of
the
failures, have
come
as
surprises.
We
will
list
here
some
of
the
aspects
of
intelligence investigated
in
Al
research
and
try
to
give
an
indication
of
the
stage
of
progress.
There
is
an
important
philosophical
point
here
that
will
be
sidestepped.
Doing arithmetic
or
learning
the
capitals
of
all
the
countries
of
the
world,
for
example,
are
certainly
activities
that
indicate
intelligence
in.
humans.
The
issue
here
is
whether
a
computer
system
that
can
perform these
tasks
can
be said
to
know
or
understand
anything.
This point
has been
discussed at
length
(see,
e.g.,
Searle,
1980,
and
appended
commentary)
and
will
oje
avoided here
by
describing
the
behaviors
themselves
as
intelligent.
without
commitment
as
to how to
describe
the
machines
that
produce
them.
Problem
solving.
The
first
big "successes"
in
Al
were
programs that
could
solve
puzzles
and
play
games.
Techniques
such
as
looking
ahead
several
moves
and
dividing
difficult
problems
into
easier
subproblems
"I
6
evolved,
respectively,
into
the
fundamental
Al
techniques
of
search
and
problem
reduction.
Today's
programs
play
championship-level
checkers
and backgammon,
as
well
as
very
good
chess.
Another
problem-solving
program,
the
one
that
does
symbolic
evaluation
of
mathematical
functions,
performs
very
well
and
is
being
used
widely
by
scientists and
engineers.
Some
programs
can even
improve
their
own
performance
with
experience.
As discussed
below,
the open
questions
in
this
area
involve
abilities
that human
players
exhibit
but
cannot
articulate,
such
as
the
chess
master's
ability
to
see
the
board
configuration
in
terms
of
meaningful
patterns.
Another
basic
open
question
involves
the
original
conceptualization
of
a
problem,
called
in
Al
the
choice
of
problemn
representation.
Humans
often
solve
a
problem
by
finding
a
way
of
thinking
about
it
that
makes
the
solution
easy,
Al
programs,
so
far,
must
be
told
how
to
think
about
the
problems
they solve
(i.e..
the
space
in
which
to
search
for
the
solution).
Logical
reasoning.
Closely
related
to
problem
and
puzzle
solving
was
early
work
on
logical
deduction.
Programs
were
developed
that
could
"prove"
assertions
by
manipulating
a
data
base
of
facts,
each
represented
by
discrete
data-stnctures
just
as
they
are
represented
by
formulas
in
mathematical
logic.
These
methods,
unlike
many
other
Al
techniques.
could
be
shown
to
be
complete
and
consistent.
[hat
is,
given
a
set
of
facts.
the
programs
theoretically
could
prove
all
theorems
that
followed
from
the
facts,
and
only
those
theorems.
Logical
reasoning
has
been
one
of
the
most
persistently investigated
subareas
of
Al
research.
Of
particular
interest
are
the
problems
of
finding
ways
of
focusing
on
only
the
relevant
facts
from
a
large data
base
and
of
keeping
track
of
the
justifications
for
beliefs
and
updating
them
when
new
information
arrives.
Programming.
Although
perhaps
not
an
obviously
important
aspect
of
human
cognition,
programming
itself
is
an
important
area
of
research
in
Al.
Work
in
this
area,
called
automatic
programning
has
investigated
systems
that
can
write computer
programs
from
a
variety
of
descriptions
of
their
purpose,
such
as
examples
of
input/output
pairs,
high-level
language
descriptions,
and
even
English-language
descriptions
of
algorithms.
Progress
has
been
limited
to
a
few.
fully
worked-out
examples.
Automatic-programming
research
may
result
not
only
in semiautomatcd
systems
for
software
development
but
also
in
Al
programs that learn
(i.e.,
modify
their
behavior)
by
modifying
their
own
code.
Related work in
the
theory
of
programs
is
fundamental
to
all
Al
research.
Language.
The
domain
of
language
understanding
was
also
investigated
by
early
Ai
researchers
and
has
consistently
attracted
interest.
Programs
have
been
written
that
retrieve
information
from
a
data
base
in
7
response
to
questions
posed
in
English,
that
translate
sentences
from
one language to
another,
that
follow
instructions
or
paraphrase
statements
given
in
English,
and
that
acquire
knowledge
by
reading textual
material
and
building
an
internal
data
base.
Soi
programs
have
even
achieved
limited
success
in
interpreting
instructions
that
are
spoken
into
a
microphone
rather
than
typed
into
the
computer.
Although
these
language
systems
are
not
nearly
so
good
as
people
are
at
any
of
these
tasks,
they
are
adequate
for
some
applications.
Early
successes
with
programs
that
answered
simple
queries
and
followed
simple
directions,
and
early
failures
at
machine-translation
attempts,
have
resulted
in
a
sweeping
change
in
the
whole
Al approach
to
language.
The
principal
themes
of
current
language-understanding
research
are
the
importance
of
;t
amounts
of
knowledge
about
the
subject
being
discussed
and
the
role
of
e.vpeclalions,
based
on
the
si
t
matter
and the
conversational
situation,
in
interpreting
sentences.
The
state
of
the
art
of
practical lang
e
programs
is
represented
by
useful
"front
ends" to
a
variety
of
software
systems.
These
programs
accept
only
in
some
restricted
form:
they
cannot handle
some
of
the
nuances
of
English grammar
and
are
usefui
.,)
interpreting
sentences
only
within
a
relatively
limited
domain
of
discourse.
Although
there
has
been
very
limited
success
at
translating
Al
results in language
and
speech-understanding
programs
into
ideas
about the
nature
of
human
language processing
the
realization
of
the
importance
in
language
understanding
of
extensive
background
knowledge,
and
of
the
contextual
setting
and
intentions
of
the
speakers,
has
changed
our notion
of
what
language
or
a
theory
of
language
might
be.
Learning.
Certainly
one
of
the
most
significant
aspects
of
human
intelligence
is
our
ability
to learn.
However, this
is
an
example
of
cognitive behavior
that
is
so
poorly
understood that
very
little
progress
has
been
made
in
accomplishing
it
in
Al
systems.
Although
there
have
been
several
interesting
attempts
at
this,
including
programs
that learn
from
examples,
from
their
own
performance,
or
from advice
from
others.
Al
systems
do
not
exhibit
noticeable
learning.
Robotics
and
vision.
One
area
of
Al
research
that
is
receiving
increasing
attention
involves
1-
rograms
that
manipulate robot
devices.
Research
in this
field
has
looked
at
everything
from
the
optimal
movement
of
robot
arms to
methods
of
planning
a
sequence
of
actions to
achieve
a
robot's
goals.
Some
robots
"see"
through
a
TV
camera
that transmits
an
array
of
information
back
to
the
computer.
The
processing
of
visual
information
is
another
very
active,
and
very
difficult,
area
of
AI
research.
Programs
have
been
developed
that
can
recognize
objects
and
shadows
in
visual
scenes,
and
even
identify
small
changes
from
one
picture
to
the
next,
for
example,
for
aerial
reconnaissance.
The true potential
of
this
research,
however,
is
that
it
deals
with
artificial
intelligences
in
perceived
and
manipulable
environments
similar
to
our
own.
8
Systems
and
languages.
In
addition
to
work
directly
aimed
at
achieving
intelligence,
the
dcelopnieI
of
new
tools
has
always
been
an
important
aspect
of
Al
rcsearch.
Some
of
tie
most
important
contributions
of
Al
to
the
world
of
computing
have
been
in
the
fonn
of
spin-offs.
Cornputcr-s
stem,
ideas like
time-sharing,
list
processing,
and
interactive
debugging
were
developed
in
the
Al
research
enironmcnt.
Specialimcd
prograin
g
languages and
systems,
with
features designed
to
facilitate deduction,
robot
manipulation,
cognitive
modeling,
and
so
on,
have
often
been
rich
sources
of
new
ideas.
Most
recent
anmong
these
has
been
the
nuns
know
ledge-representation
Linguages.
These
are
computer
languages
for encoding
knowledge
s
data
Structures
and
reasoning
methods
as
procedures,
developed
o'
er
the
last
fie
years
to
explore
a
aiaiey
of
ideas
about
how
to
build
reasoning
programs.
Terry
Winograd's
1979
article
"'Ieond
l'rogarnmiig
Languages"
discusses
some
of
his
ideas
about
the
future
of
computing,
inspired
in
part
by
his
research
ol
Al.
Expert
systems.
Finally,
the
area
of
"expert,"
or
"knowledge-based,"
systems
has
recently
emerged
as
a
likey
area
for
useful
applications
of
Al
techniques (Feigenbaum,
1977).
Typically,
the
user interacts
w
ith
an
expert
s~stem
in a
form
of
consultation
dialogue,
just
as
he
(or
she) would
interact
with
a
human
expert
in a
particular
area:
explaining
his
problem,
performing
suggested
tests,
and
asking
questions
about
proposed
solutions.
Current
experimental
systems have
performed
very
well
in
consultation
tasks
like
chemical
and
geological
data
analysis,
computer-system
configuration,
completion
of
income
tax
forms,
and
even
medical
diagnosis.
Fxpert
systems
can
be viewed
as
intermediaries
between
human
experts,
who
interact
with
the
systems
in
knowledge-acquisition
mode,
and
human
users,
who
interact
with
the
systems
in
consultation
nouce.
F-urthenore,
much
research
in
this
area
of
Al
has
focused
on
providing
these
systems
with
the
ability
to
explain
their
reasoning,
both
to
make
the
consultation
more
accelptable
to
the
user
and
to
help
the
human
expert
locate the
cause
of
errors
in
the
system's reasoning
when
they
occur.
Because
its
imminent
commercial
applications
are
indicative
of
important
changes
in the
ficld,
much
of
the
ensuing
discussion
of
the
role
of
Al
in
the
study
of
mind
will
refer
to
tie
expert-systems
research.
That
these
systems
*
"represent"
vast
amounts
of
knowledge
obtained
from
human
experts,
*
are
used
as
tools
to
solve
difficult
problems
using
this
knowledge,
*
can
be
viewed
as
intermediaries
between
human
problem
solvers,
*
must
explain
their "thought
processes"
in
terms
that people
can
understand,
and
o
are
worth
a
lot
of
money
to
people
with
real
problems
9
are
the
essential
points
that
will
be
true
of
all
of
Al
someday,
in
fact,
of
computers
in
general, and
will
change
the
role that
Al
research
plays
in
the
scientific
study
of
thought.
Open
problems.
Although
there
have
been
much
activity
and
progress
in
the
25-)
ear
history
of
Al.
sonme
%ery
central
aspects
of
cognition
have
not
yet
been
achieved
by
computer
programs.
Our
abilities
to
reason
about
others'
beliefs,
to
know
the
limits
of
our
know
ledge.
to vi
talie
things,
to
be
"remnindcd"
of
rele'.nt
events,
to
learn,
to
reason
by
analogy,
and
to
make
plausible
inferences,
reali,e
when
they
are
wrong,
and
know
how
to
recover
from
them
are
not
at
all
understood.
It
is a
fact
that
these
and
many
other
fundatenital
cognitive capabilities
may
remain
problematic
fbr
some
time.
But
it
is
also
a
fact
that
computer
programs
hawc
successfully
achie.ed
a
le'.l
of
performance
on
a
range
of
"intelligent"
behaviors
unmatched
by
anything
other
than
the
human
brain.
Al's
ailure
to
pro%
ide
some
seemingly
simple
cognitive
capabilities
in
computer
programs
becomes,
in
the
vmcvk
of
Al
to
be
presented
in
this
paper,
part
of
the
set
of
phenomena
to
be
explained
by
the
new
scicnce.
Al
and
the
Study
of
Mind
Al
research
in
problem
solving,
language processing, and
so
forth
has
produced
some
impressive
and
usefi
computer
systems.
It
has
also
influenced,
and
been
influenced
by.
research
in
many
other fields.
What,
then,
is
the
relation
between
Al
and
the
other
disciplines
that
study
the various
aspects
of
mind.
for
example,
psychology,
linguistics,
philosophy,
and
sociology?
Al
certainly
has
a
unique
method-designing
and
testing
computer
programs-and
a
unique
goal-making
those
programs
seem
intelligent.
It
has
been
argued
from
time
to
time
that
these
attributes
make
AI
independent
of
the
other disciplines:
Artificial
Intelligence
was an
attempt
to
build
intelligent
machines
without
any
prejudice
toward
making
the system
simple,
biological, or
humanoid,
(Minsky.
1968,
p.
7)
But
one
does
not start from
scratch
in
building
the
first
program to accomplish
some
intelligent
behavior:
the
ideas
about
how
!hat
program
is
to
work
must
come
from
somewhere.
Furthermore,
most
Al
researchers
are
interested
in
understanding
the
human
mind
and
actively
seek
hints
about
its
nature in
their
experiments
with
their
programs.
The
interest
within
Al
in
the
results
and
open
problems
of
other
disciplines
has been
fully reciprocated
by
interest
in
and
application
of
Al
research
activity
among
researchers
in
other
fields.
Many experimental
L*
and
theoretical
insights
in
psychology
and
linguistics,
at
least, ha~e
been
sparked
by
Al
techniique%
dnd
results.
Fu1rtheniore.
this
flow
is
likely
to
increase
dramiatically
in
thle
future,
its
Source
is
thc
ariety
of
new
phenomena
displia
ed
by
Al systemls-the
number,
quality.
utility,
-nd
level
of
acti%
iy
of
%kihich
%%ill
soonl
dramatically
increase.
But
first
let
uts
examine
whiat
kind
of
interaction-;
have taken
place
bet%%
een
Al
I.11d
thle
other
disciplines.
1The
Lan1~!guagi
o(*opuaioni
ANs "C
definled
it
at
thle
outset.
Al
is ai
branch
of
computer
scicnce.
Its
practitioners
Are
traiined
inl thle
%ario
Ll
is
b
ields
of
comxiputecr
science:
fornmal
comnputing
theor%
rr
.githi
ox
esign,
h
iid
are
and
pc
r.
i
i
S
SIIS
steni
a cire,
programming
laniguages,
and
programming.
I'lhe
stud%
of
each
otf
these
suharcas
has
produced
a Ia
nguave
of
its
ownm.
iniidcat
inrg
our
u
nde
rstanrd
inrg
of'
the
i
inportar
t
k
nom%
plictIO111CM1
of
computing.
Thle
underlying
assumption
of
our
research
is
that
this
lainguage
(Ml
ich
in
ol~
es
concepts
like
prices,
procedure,
interpreter.
bottomn-up
and
top-downi
processing,
object-oriented
progratil
illi
1g.
And
trw~eer)
and
uic
experience
with
cotmputation
that
it
embodies
%kill,
in
tun,
riasist
uts
iii nnderst~inding
the
%,arnons
p'henome'na of
mnind.
Beflore
Ae
go
onl
to
discuss
the
utility
of
these
Computational
concepts,,
it
should
he
stated
thait.
iii
fact,
our
un
tde
rsta
nd
ing
of
computation
itself
is
quite
limited.
Von
Neumanni
(1958)
dreamred
of
an
"iniformation
theor
-
of
the
nature
of
thinking:
T'he
body
of
experience
which
has
growk
iup around
thle
planning.
emiluaitirig.
and
coding
of
cornpl
icated
longical
anrd
rmathem
at
ical
au
tornata
%
ill
hebe
l I'
us
'
ofinucII
of
tI
is
inifomiat
ion
theory
....It %i
ould
be
%cry
saiisfac
tory
it'
one
coulId
ta1k
ihout
itj
a
hcorN-
of
such
an
tonata.
Regrettably.
%what
atl
this
mioriemit
exists-arid
to
whait
I
must
appeal-can
as
yet
be
described
only
ats
an
impert'cctly articulated
arid
hardly
forialited
"body
of
experience."
(p. 2)
And
ten
years
later,
iii
their
superb
treatise
onl
perceptronlike
automnata.
NMinsky
amid
Papert
(1969)
lanment:
We
kimo
shamiefully
little
about
our
comrpulter's
amid
their
computations-..We
know
very
little.
for
irismice.
about
ho%
much
comnipuitatirn
a
job
should
require-
li
Fe
iiniturity
sho%
ii
by
our
innxbilmt
to
aiisi~er
questions
oh
this
kinid
is
exhibited
e~
ci
ii
(ie
linigua~ge
used
to
fonnilulate
thle
qunest
ions.
Worid
pairs
such
as
"parallel''
s.
''serial..
local"~
%s.
''lbl'and
-digital"
%S.
'.ianalog''
are used
ats
if
they
referr-ed
to
well-defined
technical
concepts.
Fetil
when
this
is
tre,
(lhe
technical
meaning
%aries
front
tiser
to user
amid
context
to
Context.
fiut usually
dhie%
are
treated
so
loosely
that
the
species
of
Coitputing
machine
defined
by
thiemi
belongs
to
nx
thology
rather
than
scice.
(pp.
[-2)
There
is
still
no
adequate
theory
of
corlpuition
for
understaundinig
the
nature
arid
scope
of
symnbolic
processes.
hut
there
is
rapidly
accumnulating
experienice
with computation
of
all
sorts-iseul
new
concepts
emerge
continually.
11
The
Computational
Metaphor
The
discipline
most
closely
related
to
At
is cognitive
psychology.
These
two
disciplines
deal
primarily
with
the
same
kinds
of
behaviors-perception,
memory,
problem solving.
And
they
are
siblings:
Modern
cognitive
psychology
emerged
from
its
behavior-oriented
precursors
in
conjunction
with
the
rise
of
Al.
That
there
might
be
a
relation
between
the
new
field
of
Al
and
the
traditional interests
of
psychologists
was
evident
from
the
beginning:
Our
fundamental
concern
was
to
discover whether
the
c.bernetic
ideas
have
any
relevance
for
psychology.
[he
men
who
have
pioneered
in
this
area
hjae
been
remarkably
innocent
of
psychology....
There
Must
be
some
way
to
phrase
the
ncw
ideas
so
that
thc
can
contI
ibute
to
and
profit
from
the
science
of
behavior
that
psychologists
ha~e
created.
(Miller,
Galanter,
and
Pribram,
1960,
p.
3)
What
in
fact
happened
was
that
the existence
of
computing
scr
ed
as
an
inspiration
to
traditional
psychologists
to
begin
to
theoriie
in
tenns
of
internal,
cognitive
mechanisms,
Use
of
the
concepts
of
computation
as
metaphors
for
the processes
of
the
mind
strongl
influenced
the
form
of
modern
theories
of
cogniti e
psychology-for
example,
theories
expressed
in
terms
of
memories
and
retrieval
processes:
Computers
accept
information,
manipulate
symbols,
store
items in "memory*"
and
retrieve
them
again,
classify
inputs,
recognize
patterns, and
so
on.
Whether
they
do
these
things
just
like
people
was
less
important
than
that they
do
them
at
all.
The
corning
of
the
computer
provided
a
much-needed
reassurance
that
cognitive
processes
were
real.
(Neisser,
1976,
p.
5)
The
metaphorical
use
of
the
language
of
computation
in
describing
mental
processes
was
Found
to
be,
at
least
for
a
time,
quite
fertile
ground
for sprouting
psychological
theories.
During
a
period
of
concept
formation,
we
must
be
well
aware
of
the
metaphorical
nature
of
our
concepts.
l
lowever,
during
a period
in
which
the
concepts
can
accommodate most
of
our
questions
about
a
given subject
matter,
we
can
afford
to
ignore
their
metaphorical
origins
and
confuse
our description
of
reality
with
that
reality.
(Arbib,
1972,
p.
11)
When
pioneering
work
by
Newell.
Shaw,
and
Simon
and by
other
research
joups
showed
that
"programming
Lip"
their
intuitions
about
how humans
solve
pu/zles,
find
theorems,
and
so
on
was
adequate
to
get
impressive
results,
the
link
between
the
study
of
human
problem-soh
ing
and
Al research
was
firmly
established.
Consider,
for
example,
computer
programs
that
play
chess.
Current
programs
are
quite
proficient-the
best
experimental
systems
play
at
the
human "expert" level,
but
not
as
well
as
human
chess
"masters,"
lhe
programs
work
by
searching
through
a
space
of
possible
moves,
that
is,
considering
the
alternative
moves
and
their
consequences
several
steps
ahead
in
the
game,
just
as
human
players
do.
'hese
programs,
even
some
of
12
the
earliest
versions,
could
search
through
thousands
of
moves
in tile
time
it
takes
human
players
to
consider
only
a
do/en or
so
alternatives.
The
theory
of
optimal
search,
developed
as
a
mathematical
formalism
(paralleling,
as
a
matter
of
fact,
much
of
the
work
oil
optimal
decision
theory
in
operations
research)
constitutes
some
of
tie
core
ideas
of
Al.
The
reason
that
computers cannot
beat
the
best
human
players
is
that
looking
ahcad
is
not
all
there
is
to
chess.
Since
there
are
too
many
possible
iomes
to
search
exhaustively,
even
on
tile
fastest
imiginable
computers,
alternative
moves
(board
positions)
must
be
evaluated
vithout
knowing
for
sure
which
mioe
will
lead
to
a
"inning
game,
and
this
is
one
of
those
skills
that
human
chess
experts
cannot make explicit.
Psychological
studies
have
show
n
that
chess
masters
have
learned
to
see
thousands
of
meaningful
configurations
of
pieces
"hen
thyc
look
at chess
positions,
which
presumably
helps
them
decide
ol
tile
best
move.
but
no
one
has
Net
suggested
how
to design
a
computer
program
that
can
identiFy
these
Col
figurations.
For
the
lack
of
theory
or
intuitions
about
human
perception
and
learning.
A I
progress
on
computer
chess
has
virtually
stopped,
but
it
is
quite
possible
that
new
insights
into
a
very
general
problem
were
gained.
The
computer
programs
had
pointed
tip,
more
clearly
than ever,
what
would
be
useful
for
a
cognitive
systcm
to
learn
to
see.
It
takes
many
years
for
chess
experts
to
develop
their
expertise-their
ability
to
"understand"
the
game
in
terms
of
such
concepts
and
patterns
that
they
cannot
explain
easily,
if
at
all.
The
general
problem
is
of
course,
to
determine
what
it
is
about
our
experience
that
we
apply to
future
problem
solving:
What
kind
of
knowledge
do
we
glean
from
our
experience?
The
work
on
chess
indicated
some
of
the
demands
that
would
be
placed
on
this knowledge.
Language
Translation
and
Linguistics
Ideas
about
getting
computers
to
deal
in
some
useful
way
with
the
human
languages,
called "natural"
languages
by
computer
scientists,
were
conceived
before
any
machines
were
ever
built.
lle
first
line
of
attack
was
to
try to
use
large,
bilingual
dictionaries
stored
in
die
computers
to
translate
sentences
from
one
language
to
another
(Barr
and
Feigenbaum,
1981.
pp.
233-238).
The
machine
would look
tip
the
translation
of
the
words in the
original
sentence,
figure
out
the
"meaning"
of
the
sentence
(perhaps
expressed
in
some
interlingua),
and
produce
a
syntactically
correct
version
in
the
target language.
It
did
not work.
It
became
apparent
early
on
that
processing
language
in
any
useful
way
involved
understanding.
which
in
turn
involved
a
great
deal
of
knowledge
about
the
world-in
fact,
it
could
be
argued
, .
4,
13
that
the
more
one
"knows,"
the
more
one
"understands"
each
sentence
one
reads.
And
the
level
of
world
knowledge
needed
for
any
useful
language-processing
is
much
higher
dun
our
original
intuitions
led
us
to
expect.
There
has
been
a
serious
debate
about
whether
Al
work
in
computational
linguistics
has
enlightened
us
at
all
about
the
nature
of
language
(see
)resher
and
Hornstein,
1976,
and
the
replies
by
Winograd,
1977,
and
Schank and
Wilensky.
1977).
The
position
taken
by
A[
researchers
is
that
if
our
goal
in
linguistics
is to
include
understanding
sentences
like
Do
you
have
the
thne?
and
WVel
have
dinter
afier
the khIs
weash
their
hands,
which
involve
the
total
relationship
between
the
speakers,
then
there
is
much
more
to it than
tie
syntactic arrangement
of
words
with
well-defined
meanings-that
although
the
stud%
in
linguistics
of
tie
systematic
regularities
within
and
between
natural
languages
is
an
important
key
to
the
nature
of
language
and
the
workings
of
the
mind,
it
is
only
a
small
part
of
the
problem
of
building
a
useful
language
processor
and.
therefore,
only
a
small
part
of
an
adequate
understanding
of
language (Schank and
Abelson,
1977):
For both
people and
machines,
each
in
their
own
way,
there
is a
serious
problem
in
common
of
making
sense
out
of
what
they hear,
see,
or
are
told
about
the
world.
The
conceptual
apparatus
necessary
to
perform
even
a
partial
feat
of
understanding
is
formidable
and
fascinating.
(p.
2)
Linguists
have
almost
totally
ignored
the
question
of
how
human
understanding
works....
It
has
nevertheless
been
consistently
regarded
as
important
that computers
deal
well
with
natural
language
....
None
of
these
high-sounding
things
are
possible,
of
coursc,
unless
the
computer
really
'understands'
the
input.
And
that
is
the
theoretical significance
of
these
practical
questions-to
solve
them
requires
no
less
than
articulating
the
detailed
nature
of'understanding'.
If
we
understood
how
a
human understands,
then
we
might
know
how
to make
a
computer
understand,
and
vice
versa.
(p. 8)
'T'his idea
that
building
Al
systems
requires
the
articulation
of
the
detailed
nature
of
understanding,
that
is,
that
implementing
a
theory in
a
computer
program requires
one to
"work
out"
one's
fuzzy
ideas
and
concepts,
has
been
suggested
as
a
major
contribution
of
Al
research
(Schank
and
Abelson,
1977):
Whenever
an
Al
researcher
feels he
understands
the
process
he
is
theorizing
about
in
enough
detail,
he
then
begins
to
program
it
to
find
out
where
he
was
incomplete
or
wrong
....
The
time
between
the
completion
of
the
theory
and
the
completion
of
tile
prograim
that
embodies
tile
theory
is
usually
extremely long.
(p.
20)
And
Newell
(1970),
in
a
thorough
discussion
of
eight
possible
ways
one
might
view
the
relation
of
Al
to
psychology,
suggests
that
building
programs
"forces
psychologists to
become
operational,
that
is.
to
avoid
the
fuzziness
of
using
mentalistic
terms"
(p.
365).
Certainly
the
original
conception
of
tie
machine-translation
effort,
although it
was
intuitively
sensible.
14
fell
tar
short
of
what
would
be
required
to
enable
a
machine
to
handle
language,
indicating
it
limlited
conception
of
%
hat
language
is.
It
is
in
the
broadening
of
this
conception
that
AI
has
contributed
most
to
the
study
of
language (Schank
and
Abelson,
1977,
p.
9).
lThus,
Al
can
show,
as
in
the
examples
of
chess
and
language
understanding,
that
intuitive
notions
and
assumptions
about
mental
processes
just
do
not
work.
Furthermore,
analy/ing
the
behavior
of
Al
programs
implemented
on
the
basis
of
existing,
inadequate
concepts
can
offer
hints
on
how
tie
concepts
of
the
theory
affect
tie
success
of
its
application.
Scientific
Languages
and
Theory
Formation
lawrence
Miller.
in
a
1978
article
that
reviews
the
dialogue
between
psychologists
and
Al
researchers
about
Al's contribution
to
the
understanding
of'
mind,
concludes
that
the
critics
of
Al
believe that
it
is
easy
to
construct plausible
psychological
theories,
the
difficult
task
is
demonstrating
that
these
theories
are
true.
The
advocates
of
Al
believe
that
it
is
difficult
to
construct
adequate
psychological
theories:
but
once
such
a
theory
has
been
constructed,
it
may
be
relatively
simple
to
demonstrate that
it
is
true.
(p.
113)
And
Schank
and
Abelson
(1977)agree:
We
are
not
oriented
toward
finding
out
which
pieces
of
our
theory
are
quantifiable
and
testable
in
isolation.
We
feel
that
such
questions
can
vait.
First
we
need
to
know
if
we
have
a
viable
theory.
(p.
21)
Just
as
A]
must
consider
the
same issues
that
psychology
and
linguistics
address,
other
aspects
of
knowledge
dealt
with
by
other
traditional
disciplines
must
also
be
considered.
For
example,
current
ideas
in
Al
about
linking
computing
machines
into
coherent
systems
or
cooperative
problem-solvers
forces
us
to
consider
the
sociological
aspects
of
knowing.
A
fundamental problem
in
Al
is
communication
among
many
individual
units,
each
of
which
"knows"
some
things
relevant
to
some
problems
as
well
as
something
about
the
other
units.
The form
of
the
communication
between
units,
the
organizational
structurc
of
the
complex,
and
the
nature
of
the
individuals'
knowledge
of
each
other
are
all
questions
that
must
find
some
engineering
solution
if
the
apparent power
of"distributed
processing"
is
to he
realized.
These
issues
have
been
studied
in
other disciplines, albeit
from
very
different
perspectives
and
with
different
goals
and
methods.
We
can
view
the
different
control
schemes
proposed
for
interprocess
communication,
for
example,
as
attempts
to
design
sociad
ssIcms
of
knowledgeable
entities.
Our
intuitions,
once again,
form
the
specifications
for
the
first
systems.
Reid G.
Smith
(1978)
has
proposed
a
contract
net
where
the
individual
entities
negotiate
their
roles
in
attacking
the
problem,
via requests
for
assistance
from
15
other
processors,
proposals
for
help
in
reply,
and contracts
indicating
agreement
to
delegate
part
of
tile
problem
to
another
processor:
and
Kornfeld
and
Hewitt
(1981)
have
developed
a
model
explicitly
based
on
problem
solving
in the
scientific community.
Only
after
we
have been
able
to
build
many
systems based
on
such
models
will
we
be
able
to
identify
the
key
factors
in
the
design
of
such
systems.
There
is