Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP
Path: utzoo!linus!decvax!harpo!seismo!hao!hplabs!sri-unix!narain@rand-unix
From: narain@rand-unix@sri-unix.UUCP
Newsgroups: net.ai
Subject: Reply to stan the leprechaun hacker
Message-ID: <4253@sri-arpa.UUCP>
Date: Fri, 12-Aug-83 18:28:00 EDT
Article-I.D.: sri-arpa.4253
Posted: Fri Aug 12 18:28:00 1983
Date-Received: Thu, 18-Aug-83 10:10:00 EDT
Lines: 28


I am responding to two of the points you raised.

Attribute value pairs are hopeless for any area (including AI areas)
where your "cognitive chunks" are complex structures (like trees). An
example is symbolic algebraic manipulation, where it is natural to
think in terms of general forms of algebaraic expressions. Try writing
a symbolic differentiation program in terms of attribute-value pairs.
Another example is the "logic grammars" for natural language, whose
implementation in Prolog is extremely clear and efficient.

As to whether FP or more generally applicative languages are useful to
AI depends upon the point of view you take of AI. A useful view is to
consider it as "advanced programming" where you wish to develop 
intelligent computer programs, and so develop powerful computational
methods for them, even if humans do not use those methods. From this
point of view Backus's comments about the "von Neumann bottleneck"
apply equally to AI programming as they do to conventional
programming. Hence applicative languages may have ideas that could
solve the "software crisis" in AI as well.

This is not just surmise; the Prolog applications to date and underway
are evidence in favor of the power of applicative languages. You may
debate about the "applicativeness" of practical Prolog programming,
but in my opinion the best and (also the most efficient) Prolog
programs are in essence "applicative".

-- Sanjai Narain