[From Bill Powers (950509.0815 MDT)]
Bill Leach (950509.01:42 U.S. Eastern Time Zone) --
RE: world-model-based control
The idea behind world-model-based control is fairly simple, although
implementing it can become complex. If you have a mental world-model
which contains ALL the properties of the environment relevant to
controlling a particular variable, then an output signal that drives the
world-model can also be applied to the environment, and the two should
behave in the same way.
However, the basic defect in this approach is a practical one: it isn't
really possible to model the environment in all the revelant aspects. A
true world-model would, for example, have to include all the inverse
kinematics and dynamics of the body and limbs, plus all the physical
laws that describe effects of body and limbs on other objects in the
environment, plus the physics of the affected objects, plus world-models
of the perceptual system through which we monitor effects of behavior.
And even if that could be done, we would still have the problem of
unpredictable disturbances that are left out of the world-model. In
order to use world-model-based control to accomplish umbrella-opening-
to-keep-the-rain-off, we would need a successful weather-predicting
world-model.
In world-models like the one Hans submitted, most of these problems are
bypassed. If there is a signal u that affects the world-model, it also
affects the real world -- but nothing is said about the means by which
it affects the real world, for example the properties of the actuators
(which the real world-model in a real organism would also have to
contain). Likewise, on the input side, there is a perceptual function
which is represented only as a noise generator adding to the effect of
the controlled variable on an internal representation. In a true world-
model, the model of the external world would have to include the
properties of the sensors -- for example, the lens of the eye, the
retinal computations, and any higher computations required to convert
the optical inputs into measures of variables like position or velocity.
Of course nobody can include such details in a model (anybody's) that we
can actually program and run, but we have to consider these details in
judging the practicality of any proposed model of behavior. The concept
of world-model-based control necessarily implies that all these details
must be present in the organism's world-model; I am dubious about that.
The real system is not allowed to make the simplifications and
approximations that we make in our computer models.
One way around this problem is to say that world-models come into play
only in the higher levels of organization, with the lower levels being
real-time negative feedback control systems that don't need all the
detailed modeling. At the lower levels, a limb can be positioned just by
varying a reference signal; the lower control system will overcome
disturbances without having to anticipate them. At the higher levels,
the model doesn't need the details because the controlled variables are
more abstract. But however the world-model is implemented, and at
whatever levels, behavior based on internal models is still limited even
in principle because of the impossibility of predicting all disturbances
that will make a difference. If I decide to buy IBM shares on a Monday
afternoon, I know what to do ordinarily, but there's no way I can model
the fact that the senior partner has died and my broker is at his
funeral.
What we need is data, not just blind modeling based on thought-
experiments. It's much too easy to imagine doing something that one
can't actually do in real behavior. And it's much too easy to remember
only positive instances of behaviors that fit a model and to bend the
memory to favor the model. That's why I wrote the little program about
blind tracking. This will let us see just how good a model-based
behavior can be, and will also help us to notice facts that might be
used for an alternative explanation of the behavior (such as the fact
that I, at least, control a different perceptual variable when I can't
see the cursor). I currently favor your explanation:
The example of walking through a dark room seems like a description
of what one would think of for "model based control" however, even
for those of us that "briskly" walk through pitch dark places, I
believe that is still an example of PCT. A "model" is indeed
involved but the model is setting references for current
perceptions.
Right, kinesthetic and vestibular perceptions. If there were a total
loss of perceptions of all kinds, I doubt very much that we would be
able to walk through a pitch-dark room -- or walk at all.
On the other hand, we can't just discount the idea of mental world-
models. There are too many things we do that involve making predictions
and running imaginary scenes through our heads in which remembered
properties of the world play a part. When one person calls across the
room to another person "Knight to King's Bishop six" there are mental
chessboards inside the players and onlookers, and a quite incredibly
accurate model of the state of the board at any time. Notice, however,
that these models don't have to include the details of HOW the knight
gets moved. The players know which knight is meant from the (imagined)
state of the board and the state of play. The player making the move
specifies the new reference position, but he doesn't have to specify the
means by which the new position is to be achieved. The player doesn't
have to add, " ... and don't knock the Queen over on the way." The lower
control systems will take care of that if the board is real; in an
imaginary board, collisions don't even have to be modeled.
Sitting around and making up stories isn't going to help us come up with
the right model. We must actually try the things we're talking about, in
a setting where we can record what actually happens.
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Best,
Bill P.