From: Keith Henson (firstname.lastname@example.org)
Date: Thu Jun 15 2006 - 12:06:37 MDT
At 09:06 AM 6/15/2006 -0500, Bill Hibbard wrote:
>Reinforcement learning (RL) is not a particular algorithm,
>but is a formal problem statement or paradigm (Baum uses
>the phrase "formal context"). As Baum describes in "What
>is Thought?", there are many classes of algorithms for
>solving this problem. Thus you cannot exclude algorithms,
>known or yet unknown, unless they violate the RL paradigm.
> From my first writings about AI I picked RL as my model
>for how brains work in part because it is open ended and
>there is much that I don't know about how brains work.
>Thus it is unfair for you to base your demonstration of
>failure of my ideas on some particular algorithm that I
>never claimed as adequate for intelligence.
>I also picked RL as my model because it showed an approach
>to protecting humans from AI that was different from
>Asimov's Laws, which I felt had unresolvable ambiguities.
>Yes, human brains use reason and Asimov's Laws work by
>reason. But in my view learning rather than reason is
>fundamental to how brains work. Reason is part of the
>simulation model of the world that brains evolved in order
>to solve the credit assignment problem for RL. In my view
>the proper way to protect human interests is through the
>reinforcement values in AIs. Rather than constraining AI
>behavior by rules, it is better to design AI motives to
>produce safe behavior.
I agree, though you really need to think this through. For example, an AI
having a motivation to be held in high esteem by other AIs and humans could
be a good thing. On the other hand (unconstrained) this goal in humans may
contribute to pathological megalomania and the guru
>In order to make this argument I
>did not have to specify a RL algorithm, and I didn't.
Results 1 - 10 of about 1,380,000 for "Reinforcement learning ".
Results 1 - 10 of about 249,000 for "Reinforcement learning " evolution
Results 1 - 10 of about 1,210 for "Reinforcement learning " "scientific
I had no idea that "Reinforcement learning " was such a high level
>Evolution via genetic selection is an example of RL:
>genetic mutations are reinforced by the survival and
>reproduction of organisms carrying those mutations.
Considering how many of our motivations are derived from "inclusive
fitness," you might want to restate this in gene centered terms (see Hamilton).
>scientific method is another good example: theories are
>reinforced by whether their predictions agree with
The scientific method is also classed as a meta-meme, i.e., a meme which
influences the survival of other memes.
>The RL paradigm is pretty general and can be
>implemented by a wide variety of algorithms. I believe
>that human brains work according the RL paradigm, using
>very complex and currently unknown algorithms, and hence
>demonstrate the adequacy of the RL paradigm for
You might start classing them into ones of genetic origin and ones of
experience, though the two classes are not entirely distinct.
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