NetNews Usenet Archive 1992 #19/NN_1992_19.iso/spool/bit/listserv/csgl/858

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Date:         Mon, 31 Aug 1992 13:10:48 +0100
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From:         HANS BLOM <[e-mail]>
Subject:      what is 'control'
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[Hans Blom, 920831]

Although I have been listening  in on this list  for some years now,  I
have  never actively contributed. I  enjoy reading the  list. You are a
very creative  and inspiring bunch of people. Being a control engineer,
PCT is a 'so  what' thing for  me, nothing new.  Some of the fields  of
application, psychology  and (although implicit)  philosophy, fascinate
me. Allow me one contribution and I promise to keep silent again for  a
long time. Besides,  it already takes me  far too much time  to monitor
the list; I wonder how  many hours there are  in a day for some  of the
people  who are  the most  active on  this  list. I  admire you,  but I
cannot follow you: too many other things to do.

Some general  remarks first. One:  control is not  everything. There is
also  a  lot of  non-control in  the  world. Two:  what do  we  mean by
'control'? Do PCT and Skinner talk  about different things or are  they
just  different perspectives? Three: where  does control come from? How
does it originate?  Four: when you are  explicit and build models,  the
type of control  that you use seems  to be just the  old-fashioned type
PID-control. There  is  a lot  in  favor of  PID-control,  but a  great
variety of  other types of  control have been  explored since: adaptive
control, dual control, robust control, to name a few.

Also, when I  read the things  that you discuss  in this list, I  often
notice  that  I see  some  things very  differently  and/or that  I see
different things. Take  one of your  popular examples:  the control  of
movements  by  e. coli.  I  remodelled and  reprogrammed  this 'control
system' from the  descriptions that I found in the  list discussions of
the last few months. My model looks as follows:

-   The environment is a point source of nourishment (let's take sugar)
    at  x = 0,  y = 0.  The   concentration  profile  in  coli's  (two-
    dimensional) environment  is an inverse square  law (different laws
    do  not   make  much   difference).  Coli   can  sense   the  sugar
    concentration  at the point where  it is. The  concentration at the
    point  source is too high for coli  (poisoning), far away it is too
    low (hunger).  A radius of 10  is optimal for coli,  i.e. coli will
    'control for'  a radius  of 10. At  a radius of  100 or  more, coli
    cannot  sense  the  sugar  concentration  anymore.  Coli's  initial
    position is at a radius of 50.

-   Per iteration coli does the following:
    1.  Coli tumbles, i.e. selects a new random direction.
    2.  Coli swims, i.e.  takes a step in the new random direction. The
        step   size  is   inversely   related  to   its   sensed  sugar
        concentration minus its optimum sugar concentration. This makes
        for large steps in case of hunger or poisoning, and small steps
        if coli  feels fine. The  step size is,  however, limited  to a
        maximum value  (when coli is  at a radius  of 100  or more) and
        also to  a (small)  minimum value. This  minimum value  ensures
        that  coli will  always have  to  reestablish its  position and
        cannot relax  after reaching  the goal. Alternatively,  a small
        random displacement of  its position (modeling physical effects
        of water flow and such) provides a similar kind of disturbance.
  Pick  your parameters as you like (their  values do not matter much),
  but make  sure that coli needs at least  some ten steps from a radius
  of 50 to a radius of 10 but not more than a few hundred.

Looking at coli's  behavior in  a number of  simulation runs, I  notice
the following. In  about half of the  simulations, coli takes  off into
the blue beyond, where its  sensors do not work anymore and where it is
essentially lost, despite  the fact that  its initial  position and  at
least its initial  ten to twenty  steps are within  the area where  its
sensors do a  perfect job. In the  other half of the  simulations, coli
goes  towards the  radius of 10  and finds  it or almost  finds it. The
path often  looks very  crooked with  lots of moves  towards, and  then
again away  from, the optimal  radius. Sometimes coli  reaches a radius
of 11 or 12  but subsequently moves away again and  gets lost. In other
cases,  coli  finds the  radius  of 10,  lingers  there for  some time,
escapes,  returns,  repeats  this  sequence  a  number  of  times,  but
eventually  it  escapes and  gets  lost in  what  for  it is  infinity.
Looking  at  the  simulation as  it  unfolds  is  an esthetically  very
satisfying  experience,  like  art:  you   _almost_  believe  that  you
'understand'.

Now what I  see is very reasonable behavior given  the real environment
in  which a real coli lives.  But it is not control.  You might call it
an attempt to  control. Coli  has more  control than  an inert  protein
molecule, but  not much.  Coli is not  fully dependent on  the Brownian
movement of the molecules of the water it  lives in: it looks as if  it
has  some small  say in  the matter.  I  look at  coli's behavior  as a
demonstration  of  _emergence_,  of how  control,  in  an  as yet  very
primitive form, comes into being in the tiniest organisms.

It is not difficult  to improve upon coli's control,  but that requires
additional equipment, either  extra sensors or memory.  Either provides
a higher-dimensional  view of the world and, given the right actuators,
therefore also actions in more dimensions than before.

Better goal  directed behavior  results if I  give my simulated  coli a
memory   (just   a  single   bit)  that   remembers  whether   the  new
concentration is 'better' than the old. If  so, I make coli continue in
the same  old  direction. If  not,  I make  coli  select a  new  random
direction. The  result is that the new coli (e. coli seems too small to
have such  a memory,  but this  could be  the model  of a larger  sized
bacterium) takes  only very _short_  moves away from  but _much longer_
moves towards  its optimum environment.  Eventually, this  new organism
reached the radius of 10 in all  simulations, and each one stayed close
to it forever.

But its  'towards'  is  not  a  'steepest  descent  towards'.  That  is
possible  only  with  another sensor,  with  which  a  gradient can  be
established. When I give my  simulated coli the capability to sense and
use the  sugar concentration gradient  (again, e. coli  cannot do that,
but something  the size  of an  amoeba can),  it immediately  takes the
shortest path to its optimum and stays there ever after.

These  simulations  give  a  lot  to  think  about.  First,  I  see  an
_emergence_ of control,  from no control at  all (type 1; in  a protein
molecule, virus or  small bacterium with no  own modes of movement)  to
the primitive partial  control that I see in my simulated coli (type 2)
to the  gradient descent (type 3) to the  steepest descent (type 4). In
reality,  of course, there  are no  discrete types, and  even an oxygen
atom can be said to 'control  for' the kind of chemical reactions  that
it will allow.  This makes 'control' a rather fuzzy  issue, very unlike
a  set of  linear differential equations  with fixed  coefficients from
which you can calculate P, I and D-terms.

Contrast penni  sibun (920818.2000)'s general  description of  type 1-4
behavior

>i don't  think behavior is ``what *needs* to  be done.''  i think it's
>what *is* done.

with Rick Marken (920819.1000)'s more restricted  description of type 4
behavior

>I have spent the last ten years trying to develop demonstrations that
>would show that precisely that assumption is wrong.

Is 'leaning on  the world' a more  appropriate term than 'control'  for
type  1  behavior?  For   which  types  of  behavior  is   Rick  Marken
(920819.1000)'s

>The computer, using "the  test for the controlled variable",  can tell
>which of these behaviors is being done intentionally--so it  is reading
>the subject's intention (mind)--hence, the program does "mind reading".

appropriate?  Are  often  posed  questions  like  "Why  is  there  such
reluctance  on  the part  of  those working  on  the hot  approaches to
behavior to  even consider the possibility that behavior is the control
of perception?" (this one  from Rick Marken) showing a  confusion about
type 1 versus type 4 behavior?

Second,  these  four  types  of  control  have  little to  do  with  an
'increase of loop gain'. A larger loop gain for coli would  mean larger
strokes and the real danger of  moving past a food source so fast  that
it cannot  be located.  Actually, coli's  loop gain is  very robust;  a
large range  results in almost  the same almost  optimal behavior. Give
it a too  small loop gain and it cannot move;  give it a too large loop
gain and it gets lost immediately.

Third, the  kind of  control that is  being discussed  in this list  is
mainly the  type 4  control. But  even in  humans I  see all  different
types. Sometimes there is nothing you can do; you are just  swept along
with the  winds of what  is for you  just random change.  Sometimes you
have a small say in the matter; you  only have a vague 'holistic' sense
the too  complex situation  and  hardly know  what  to do  (you  cannot
convert  what you  sense  into meaningful  actions). Sometimes  you can
tell when a  new situation is 'better' but not the direction of 'best'.
Sometimes you do  know what is 'best',  i.e. in which direction  to go.
All this  has to  do  with the  possibilities and  limitations of  your
equipment,   sensors,  memory  cells,  actuators  and  the  connections
between  them. In  a sense,  sensors  and memory  cells  have the  same
function  in that  the latter  can be  viewed as  sensors that  provide
access  to  (an  encoding  of)  past  experiences.  Both  increase  the
dimensionality of the  'impression' that an  organism can  have at  any
moment of time.  If the dimensionality  of the  impression becomes  too
low  relative  to  the  complexity  of  the  problem,  when  processing
capabilities  are  too limited,  when actions  are  futile, or  when no
clear goal exists, penni sibun (920825)'s

>why conduct elaborate deductions about yr surrounding when
>you can look and see?  in particular, why maintain elaborate control
>structures when you can look and see what needs to be done?  why make
>highly detailed Plans when you can improvise?  why required instant
>expertise when you can improve by just keeping on doing it?  why try
>figuring it out yourself when you can collaborate w/ others who have
>been there?  why insist on figureing out every situation afresh when
>you can trust yor accumulated experience?

strategy, sometimes called  'intuition', may work  best. But note  that
the  overwhelming  majority  of our  experience  has  been  accumulated
through our  evolutionary path through the eons, and that therefore our
problem  solving methods will be  those that are  best for _humans_ (in
my opinion, even that is frequently too broad a generalization).

Fourth,  we  experience the  behavior  of simulated  coli  as extremely
complex, despite  the fact that  the 'laws' on  which that behavior  is
based  are  extremely  simple.  That  is  because  coli's  behavior  is
unpredictable, 'chaotic'. We  cannot nicely formulate the  link between
the  randomness of its steps and the  emerged (limited) order except in
subjective  terms  such as  'sometimes some  colis  seem to  like being
around the  radius of 10'. 'Going down a  level' for an explanation is,
because it is  so non-obvious,  a never-ending  investigation into  the
randomness of  nature. Often, what  remains are  statistics, a  subject
that most of you seem not to like. But in physics, statistics is  quite
acceptable as a  tool to obtain a sufficiently accurate  picture of the
world, such as  in statistical mechanics  which does  not need  quantum
theory  to  arrive  at  'emergent'   quantities  like  temperature  and
pressure. Here,  'going up  a level' is  just as impossible,  or simply
too  difficult  for  our  limited  human  resources.  The  'three  body
problem'  from  celestial mechanics  is  a classic  example,  and chaos
researchers will  tell  you  that 'almost  all'  problems are  of  this
nature. It seems that  the proportion of analyzable problems  in nature
is vanishingly small. We may have to 'kludge' forever.

When you  only have a hammer, you  see nails everywhere. I  do not want
to detract from  the value of control  theory (a hammer is,  after all,
sometimes a very useful  instrument), but when you have  control theory
_only_, you see  control systems  everywhere. 'Control'  is an  elusive
thing, just  like 'temperature'. Control 'exists' because we want it to
exist, maybe even  because we _need_  it to exist  to bring order  into
our  chaos.  In nature,  the  mechanisms that  compose what  we  call a
control system  often seem to  be thrown together  from the lower-level
stuff that happened to  be available in  an almost random, still  badly
understood  process  that   we  call   evolution.  Most   emphatically,
evolution has no  goal; it just looks  that way to the  naive observer.
If there is no goal, 'control' may be as naive a concept.

Avery.Andrews says

>This discussion is gotten completely out of hand, and, like Martin,
>I'm rather baffled by it.  I suspect that people haven't done enough
>homework on  the  other  guys' stuff  to  justify the  things  they're
>saying about it.

I agree. A  lot of confusion  and misunderstanding  arises when  people
speak a  different  language.  Learning  each  other's  language  is  a
prerequisite for  an exchange of thoughts. Regrettably, this is a life-
long process.

Hans Blom

Who am  I? I  studied electrical  engineering, majoring in  measurement
and  control theory. I work  at a university and have  done so for more
than  20  years. My  tasks  are  both teaching  and  doing  research in
'medical engineering'.  I  have  done work  in  a number  of  subjects:
modelling,  parameter   estimation,  control,  adaptive  control,  dual
control,  man-machine interfacing  (now  called 'ergonomics'),  usually
applied  to  anesthesia  and  intensive  care  monitoring  and  control
systems. My  long-term project is  called 'servo-anesthesia',  but that
has  proven  to be  a  very  elusive  goal.  I have  looked  around  in
artificial  intelligence, mainly  expert  systems, neural  networks and
genetic algorithms.  Also psychology, physiology, and  some philosophy.
My Ph.D.  work was  the design of  a real  time expert systems  toolbox
(special purpose programming  language, its compiler and  its inference
engine).  An application was the design of an expert system based blood
pressure control system,  where the computer  controls the  flow of  an
infusion pump  that delivers a drug  in such a way  as to stabilize the
patient's blood pressure at a lower than normal level. The core  of the
system  is a PID-controller,  but that core is  surrounded by a 'safety
shell' that  contains expert  knowledge, both  medical (what  to do  on
special  occasions  such  as  shock)  and  about  how  to  tune  a PID-
controller. Tuning must  keep the controller  both fast  and stable  in
the face  of large variations  in the patient  parameters. Difficult to
handle is the  variability of the  patient's sensitivity  to the  drug,
which can  vary unpredictably by  a factor of about  80. Most difficult
is, however,  that we  cannot explicitly specify  what is best  for the
patient.

What  impressed  me most  in  my  research  was the  influence  of  the
_unknown_.  Some   examples:  how  to   learn  the   initially  unknown
characteristics of  the patient as  fast as possible  (time, especially
medical  time,   is  money)  when  the  controller  is  started,  while
guaranteeing safety  (predictable, 'almost'  optimal behavior); how  to
keep  track   of  changes  of   the  patient's   characteristics  while
controlling  stably  (no  test  signals);  and  how to  handle  missing
feedback measurements (a temporarily broken  feedback loop) while still
ensuring safety  for the patient. That cannot always be done. Sometimes
the  system has to  alarm and  transfer control back  to the physician.
You have to design  in what to do when more and more sensors fail. That
is not easy.

My expert  system's  logic  has three  truth  values: true,  false  and
unknown. Ensuring that the  system reaches a conclusion of  either true
or false ('is  the patient OK, yes  or no?') but not  the uninformative
'unknown' when given  the largest number of  input 'unknown's generally
does not seem  a mathematically tractable  problem. Still,  we want  to
design solutions.  Creativity  seems  the  only method,  but  one  that
cannot be formalized, regrettably.

Again, control is  not everything, and PCT  is even more limited.  Just
one example.  'Dual control' theory  considers optimization  of control
over some time  in an uncertain world. It  recognizes that, in general,
control  will be  better the  more accurate  a  model of  the world  is
available.   Thus,   in   many    cases   _active_   learning   (system
identification)  is  called   for.  Active  learning,   however,  means
introducing 'test  signals' and thus a  disturbance of the observation,
which  of course _degrades_  the quality  of control.  But only  in the
short run. Over  a long time, the  observations of the response  of the
test signals yield an  improved model which provides a  better control.
PCT  does not consider  this active learning.  Its 'reorganization' is,
if  I  understand correctly,  a random  process  that only  occurs when
errors  remain  large  for   some  time.  There  is  no   provision  to
temporarily  _create_ small  errors in  order to  prevent later  larger
errors.  This type  of  active  learning  ('curiosity',  'exploration',
'play') is pervasive in (higher) animals and humans.

Wittgenstein once  remarked that people  cannot think  unlogically. For
me this means  that every remark,  however stupid  it initially  seems,
has  its point and  can be  learned from  (maybe that's  why I  read so
slowly). Sometimes  others have  just  a different  perspective on  the
same 'truth'. Sometimes  someone has an impression that we  can show to
be inconsistent with  what science shows us. But even then I think that
we have to take that impression  seriously and try to find out where it
comes from and what are the sources of misunderstanding.