Re: killer apps

From: John Lloyd <jwl_at_cs.bris.ac.uk>
Date: Thu, 10 Jul 1997 11:34:46 +0100 (BST)

Manuel wrote:

> > 1. Constraints.
> > [...]
> > 2. Machine learning.
> > [...]
> > 3. Knowledge-based Internet applications.
> > [...]
>
> re 1: I agree on the constraints applications, i.e., FLP as the better
> LP.
>
> re 2: Although, I am (very) far from being an expert in machine
> learning, I think, a large number (if not most) of the big
> applications there are written in Lisp and not in an LP language. Of
> course, you can argue that FLP also generalizes FP, but I am not sure
> if that really is much of an advantage for machine learning. The
> reason is that if you want to be successful in machine learning you
> better avoid (exhaustive) search, instead many heuristics and good
> classification routines (like Quinlan's C4.5) are important.

The majority of current ML applications are propositional. However, there
is a strong move at present to apply first order learning. This is mainly
in the ILP (Inductive Logic Programming) field. They have some impressive
fielded applications. (Incidentally, Quinlan is heavily
involved in this thru the FOIL system.) Why do we want to move to first
order? Because it's much more expressive. The ILP people have strong arguments
to support this answer. So first order learning is harder than propositional
learning, but you definitely want to do it anyway.

But there are good arguments to go further. We will move to higher order
learning for exactly the same reason - greater expressive power. One
higher-order construct that is really attractive here is sets. There are
two aspects to this. First one gets an expressive logic in which to learn the
theory. Second, in contrast to neural network approaches, one actually gets a
theory! The non-symbolic approaches to learning are very powerful
and effective, but there is no useful way to explain what they have learned.
In symbolic learning you get a theory you can study. The more expressive
it is the better.

In fact, the ILP people have touched briefly on this. A paper by Muggleton
and Page makes the case for higher order. Unfortunately, it's not convincing
as they propose an uncritical use of Prolog for this, so it turns into a mess.
A good flp language is needed here.
 
I think the clear conclusion one can draw by following the history of this
is that one day, perhaps soon, we will be learning higher-order theories.

I enclose below a short position paper which makes the case for flp in ML.



> re 3: I don't see the advantage of FLP over FP here (the examples you
> gave are very much database-oriented, so maybe SQL is the
> canditate...)

The applications that can be handled by SQL aren't at all interesting
for us. I'm talking about serious knowledge base applications involving
sophisticated query answering, uncertainty, non-monotonic reasoning, and so on
- just the kind of applications that LP languages are good for. The technology
for doing this effectively on the Web hardly exists at the moment, but some
LP researchers have begun to move in the right direction. I think
we have a real opportunity to find a significant niche here.

John

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Received on Thu Jul 10 1997 - 13:19:00 CEST

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