On 16 Jun 2011, at 18:36, meekerdb wrote:
On 6/16/2011 7:38 AM, Bruno Marchal wrote:
Concerning the learning competence of a machine, I measure it by
the classes of computable functions that the machine is able to
identify from finite samples of input-outputs. This leads to the
"computational learning theory" or "inductive inference" theory,
which shows that the possible competences form a complex lattice
with a lot of incomparable competences, and with a lot of
necessarily non constructive gaps existing among them.
Do you have some reference where this is explained?
My favorite paper on this is:
CASE J. & SMITH C., 1983, Comparison of Identification Criteria for
Inference. In Theoretical Computer Science 25,.pp 193-220.
But since, there has been a ton of papers published. Notably the COLT
There is also the book:
OSHERSON D.N., STOB M.and WEINSTEIN S., 1986, Systems that Learn, MIT
press. (New edition exists since)
It is a recursion theoretic based field. Of course it does not
interest so much the engineers as most result are not constructive,
indeed necessarily so.
A basic fundamental paper is:
GOLD, E. M., 1965, Limiting recursion, Journal of Symbolic Logic, 30,
1, pp. 27-48.
GOLD E.M., 1967, Language Identification in the Limit. Information &
Control 10, pp.
Another one is:
BLUM L. & BLUM M., 1975, Toward a Mathematical Theory of Inductive
Information and Control 28,.pp. 125-155.
There is a full chapter on this in "Conscience et mécanisme". You will
find other references in the "bibilographie générale" pdf.
The gap G* minus G formalizes an easy set of inferable but non
provable self-referential truth by inductive inference type of Löbian
machines (the 'mystical machines').
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