- ... LEARNING1
- IN
X. YAO, EDITOR,
EVOLUTIONARY COMPUTATION: THEORY
AND APPLICATIONS. CHAPTER 3, PP.81-123, SCIENTIFIC PUBL. CO., SINGAPORE,
1999.
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- ... actions2
- Leslie Kaelbling
sometimes refers to this as ``writing on the walls''
but says that the ``real'' name is ``stigmergy''
(personal communication, 1994/1995).
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- ...
``true''3
-
I am not talking about fixed learning algorithms
for adjusting the parameters of others.
For instance, GAs are sometimes used to
adjust learning rates of gradient based neural nets, etc.
Or a neural net is used to compute the weights of
another neural net.
In the literature, one can find quite a few approaches
of this kind (too many to cite them all -- I settle by citing none,
not even my own).
Although such approaches sometimes may have their merits,
they do not deserve the attribute
``self-referential'' --
the additional level typically just defers the credit
assignment problem.
However, there were a few apparently more general approaches.
For instance, Lenat
[17]
reports that
his EURISKO system
was able to discover certain heuristics
for discovering heuristics.
His approach,
however,
as well as all other
previous approaches I am aware of,
were either quite limited (many essential aspects
of system behavior being unmodifiable),
and/or lacked a sound, convincing global credit assignment strategy
(as embodied by the SSA pushing and popping processes).
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- ...
experience4
-
Solomonoff appears to be well aware of problems with the meta-version:
at the end of his 1990 paper,
he refers to self-improvement as a ``more distant goal'':
``The kind of training needed involves more mathematics
and work on various kinds of optimization problems -- ultimately
problems of improving computer programs.''
Another ``more distant goal'' mentioned by Solomonoff
is to let the system work
``on an unordered batch of problems -- deciding itself
which are the easiest, and solving them first''.
Note that SSA
addresses both goals,
without depending on a meta-version of universal search.
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