It **could** be that the only way a system can give rise to
probabilistically sensible patterns of action-selection, given
limited computational resources, is to do stuff internally that is
based on nonlinear dynamics rather than probability theory.
But, I doubt it...
The human brain may work that way, but it is not the only (nor the
ideal!) cognitive system...
Hmmm.... but what I wanted was to try to get some traction on why
you would say this.
Your answer is only "I don't think so."
Your comment that the human brain "... is not the only (nor the
ideal!) cognitive system" is a direct rejection of the idea that I
was asking you to consider as a hypothesis.
I *know* you don't believe it to be true! ;-) What I was trying to
do was to ask on what grounds you reject it.
It seems to me that there are some aspects of human cognition that
are reasonably well modeled using a combination of probability theory
with various other heuristics (such as "support theory").
Representation of, and reasoning about, declarative knowledge is an
example. There is also increasing evidence for Bayesian inference
being emergent from the dynamics of spiking neural nets with timing-
dependent potentiation. I believe one can show that approximations
to probabilistic term logic deduction also emerge from this sort of
neural net model.
On the other hand, there are other aspects of human cognition that
seem poorly modeled using probability theory, for instance learning
of motor procedures, attention allocation, and creativity in general
and perceptual pattern recognition. It's not so much that these
contradict probability theory, but rather that the simplest
mechanisms for implementing/describing these processes seem to
involve a lot of other tools as well as probability theory.
These observations and others inclined me to try to work out an AGI
design that combined
-- probability theory for reasoning on declarative knowledge
-- evolutionary learning (combined with prob. modeling) for procedure
learning
-- hierarchical pattern mining for perceptual pattern recognition
(defaulting to prob. reasoning and ev. learning for unusual hard cases)
-- simulated economics for attention allocation, using results of
probabilistic reinforcement learning as data where available
-- statistical pattern mining based self-analysis to recognize
emergent attractors in the overall system dynamics and embody these
attractors as declarative knowledge
I.e., I use explicit probability theory in Novamente where the brain
seems to be doing something roughly probability-theory-like, and
other tools in other places, loosely modeled on the sorts of tools
the brain seems to be using in these other places.
-- Ben
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