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On 6/8/2014 3:25 PM, John F Sowa wrote:
I came across a book by Bob Kowalski, who makes a strong argument
for model building as a basis for intelligent systems. The ideas
are further developments of his work on logic-programming systems.
Kowalski, Robert (2011) Computational Logic and Human Thinking: How
to be Artificially Intelligent, Cambridge: Cambridge University Press.
John,
There have been a number of people who have put forward that
concept. It seems to be a common basis for the implementation
myth. AKA: if you only did this then you would have AI. This was
one of the leading contributors to the 5th Generation AI failure.
The designers felt that if you implemented logic then you would
have a smart machine. It is a variant of the Philosopher's
Disease: if you implement all the words in a dictionary, then you
will have a smart machine.
Certainly there is a need for modeling, and likewise, there are
roadblocks to doing that right now. We don't know what type of KR
to use, we don't understand how the models should be structured,
and we don't know the details of extensibility.
There is no dearth of programming languages. We now have as many
computer languages as we do natural languages. That is because
they are increasingly easy to construct with tools provided for
just that purpose. Also, there are efficiency justifications for
creating new languages. In fact, my natural language is not
particularly efficient. The estimates are that English is about
50% redundant...for very good reasons. Tell me again why we need a
new computer language? I would rather vote to kill off some of the
languages we already have.
Why do you think this work is important? Or, why is this approach
more important than any other type of solution? Does it move us
forward an order of magnitude in any way?
-John Bottoms
FirstStar Systems
Concord, MA USA
You can buy it for $44.10 or download an earlier version for free:
http://www.doc.ic.ac.uk/~rak/papers/LPS%20revision.pdf
The papers cited below go into more detail about LP methods for
deriving imperative procedures from declarative statements.
Logic Programming
http://www.doc.ic.ac.uk/~rak/papers/History.pdf
Completeness of a Reactive System Language
http://www.doc.ic.ac.uk/~rak/papers/KELPS%20Completeness.pdf
Reactive Computing as Model Generation
http://www.doc.ic.ac.uk/~rak/papers/LPS%20revision.pdf
See below for excerpts from each of these sources.
John
_________________________________________________________________________
From the book:
Although the applications of Computational Logic in AI require the
use of mathematical notation, its human applications do not. As a
consequence, I have written the main part of this book informally,
to reach as wide an audience as possible. Because human thinking
is also the subject of study in many other fields, I have drawn upon
related studies in Cognitive Psychology, Linguistics, Philosophy,
Law, Management Science and English Composition.
In fact, the variant of Computational logic presented in this book
builds not only upon developments of logic in AI, but also upon many
other complementary and competing knowledge representation and problem
solving paradigms. In particular, it incorporates procedural representa-
tions of knowledge from AI and Computing, production systems from AI
and Cognitive Science, and decision analysis from Management Science,
Cognitive Psychology and Philosophy.
From the article, History.pdf
The driving force behind logic programming is the idea that a single
formalism suffices for both logic and computation, and that logic
subsumes computation.
From the article, KELP Completeness.pdf
KELPS is a first-order, sorted language, including a special sort
for time. In the version of KELPS presented in this paper, we assume
that time is linear and discrete, and that the succession of time
points is represented by the ticks of a logical clock.
From the article, LPS revisions.pdf
In this paper we propose a logic-based framework inspired by artificial
intelligence, but scaled down for practical database and programming
applications. Computation in the framework is viewed as the task of
generating a sequence of state transitions, with the purpose of making
an agent’s goals all true.
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