STAR@LAVALVM1.BITNET (Spencer Star) (04/21/88)
Exiting work in AI. There's lots of it. The three criteria
are: 1. Highly thought of by at least 50% in the field.
2. Positive contribution
3. Real AI
Machine learning is a real AI field; there is general agreement
that learning is central to real AI. Machine learning is perhaps
the major subfield devoted to AI learning, although most other
subfields also touch upon learning. Some, like neural networks,
are centered on learning.
Surprisingly, I don't see that much controversy in machine learning. There
is solid progress being made on several fronts. Recent controversies have
been on esoteric issues like whether there is a tradeoff between generalization
and efficiency, whether facts in the deductive closure of a system can be
said to be learned, etc. No big battles with rival camps raging at each
other.
There is, however, solid research. Hal Valiant and David Haussler have
made good theoretical progress at defining a certain type of learning.
Explanation-based learning is a very exiting hot!!! area for research
right now. At the Stanford Symposium many people made progress reports
on hybrid systems that use the deductive inference engine based on
PROLOG-EBG or EGGS or some variant, and then include inductive techniques
to do learning on both deductive and inductive levels. Another area
involves classification trees of the sort generated by Quinlan's ID3
program. There is wide agreement that this is a positive contribution.
And it is not a controversial technique.
SOAR is an architecture being worked on by people at severl universities.
Although the claims of the group have been controversial, the actual
work they are doing is well thought of. And copies of the program
are available to researchers for their own experimentation.
Take your pick. There is lots to choose from.
Spencer Star