Very interesting article. Yes, the controller is not intelligent but you have to factor in the designer. (I think this is something like a response to the Chinese Room argument). Just a few comments:
It has no model of its surroundings.
It has, a very simple one: the sign of the gain of the plant (steady-state).
It has no model of itself.
No, but its maker does: the transfer function of the controller.
It makes no predictions.
As in the first point: implicit in the design of the system is that temperature goes up with +1 output. If you flip the sign you get positive feedback and the system does not work as intended.
It has no priors.
Its designer knows some a priori things, like the typical time constant of the temperature trajectory and its range.
It has no utility function.
Maybe not a formal one, but you could build one with things like integrated squared error.
Concerning your first point, that the designer has to hand-insert that all-important sign bit.
So how do humans come up with these sign bits?
I imagine a trial-and-error process of interacting with the controlled system.
During this, the person’s brain is generating an error signal derived directly or indirectly from an evolutionarily-fixed set point.
While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be.
If the situation is exponential and the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently energized, and if negative, to the energized muscle itself.
Bayesian inference would enter as a Kalman filter used to calculate the controlled variable.
I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter,
so some kind of bootstrapping process could be involved.
In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard-wired set point itself a controller?
I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.
Concerning your first point, the designer has to hand-insert that all-important sign bit. So how do humans come up with these sign bits? I imagine a trial-and-error process of interacting with the controlled system. During this, the person’s brain is generating an error signal derived over learning time by classical conditioning from an evolutionarily-derived hypothalamic error signal. While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be. If the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently moving, and if negative, to the moving muscle itself. Bayesian inference would enter as a Kalman filter used to calculate the controlled variable. I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter. In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard wired error signal itself a controller? I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.
Very interesting article. Yes, the controller is not intelligent but you have to factor in the designer. (I think this is something like a response to the Chinese Room argument). Just a few comments:
It has, a very simple one: the sign of the gain of the plant (steady-state).
No, but its maker does: the transfer function of the controller.
As in the first point: implicit in the design of the system is that temperature goes up with +1 output. If you flip the sign you get positive feedback and the system does not work as intended.
Its designer knows some a priori things, like the typical time constant of the temperature trajectory and its range.
Maybe not a formal one, but you could build one with things like integrated squared error.
Concerning your first point, that the designer has to hand-insert that all-important sign bit. So how do humans come up with these sign bits? I imagine a trial-and-error process of interacting with the controlled system. During this, the person’s brain is generating an error signal derived directly or indirectly from an evolutionarily-fixed set point. While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be. If the situation is exponential and the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently energized, and if negative, to the energized muscle itself. Bayesian inference would enter as a Kalman filter used to calculate the controlled variable. I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter, so some kind of bootstrapping process could be involved. In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard-wired set point itself a controller? I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.
Concerning your first point, the designer has to hand-insert that all-important sign bit. So how do humans come up with these sign bits? I imagine a trial-and-error process of interacting with the controlled system. During this, the person’s brain is generating an error signal derived over learning time by classical conditioning from an evolutionarily-derived hypothalamic error signal. While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be. If the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently moving, and if negative, to the moving muscle itself. Bayesian inference would enter as a Kalman filter used to calculate the controlled variable. I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter. In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard wired error signal itself a controller? I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.