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AIList Digest Volume 5 Issue 039
AIList Digest Wednesday, 11 Feb 1987 Volume 5 : Issue 39
Today's Topics:
Queries - LISP Conversion & Symbolics Termcap,
AI Tools - Coral Object Logo & MICE Expert System Shell,
Education - Introductory AI Books,
Representations - Richness and Flexibility
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Date: Mon, 09 Feb 87 11:00:18 -0800
From: simmons@aerospace.aero.org
Subject: LISP Conversion
I am gathering information concerning the conversion or
translation of programs written is LISP to procedural languages
(especially interested in LISP to Fortran). I would appreciate
comments from anyone who knows of work being done in this area.
I will summarize replies for the AILIST.
Thanks, Charles Simmons (simmons@aerospace.arpa)
------------------------------
Date: Tue, 10 Feb 87 09:18:16 -0800
From: Amnon Meyers <meyers@CIP.UCI.EDU>
Subject: symbolics question
I'm having trouble getting the Symbolics 3600 to behave
properly as a terminal, when logged into UNIX systems.
Editors like VI and EMACS don't work right, even though
vt100 emulation mode is set up.
If someone has a TERMCAP line that works well, or can
otherwise help, I'd appreciate it.
Thanks,
Amnon Meyers
------------------------------
Date: 9 Feb 87 15:15:09 GMT
From: mdc@EDDIE.MIT.EDU (Martin Connor)
Subject: Coral Object Logo
In article <1725@PUCC.BITNET> 6065833@PUCC.BITNET writes:
> Can anyone recommend another LOGO for the macintosh? Has anyone found
> a way to print graphics windows on >a laserwriter? Any information
> would be greatly appreciated.
Object Logo from Coral Software in Cambridge, Mass is a good value.
It is about $80 and has loads of features, Comes with Finder 5.3 and
system 3.2, lots of examples, a good reference manual, and is
supported by a solid bunch of hackers (I know some of them).
They have an ad in this month's MACWORLD, with ordering info.
I've used it, and I recommend it highly. I hope some schools pick up
on it and use it.
------------------------------
Date: Mon 9 Feb 87 18:08:06-PST
From: Matt Heffron <BEC.HEFFRON@USC-ECL.ARPA>
Subject: MICE Expert System Shell
We ordered the $20 MICE system. I haven't used it yet, as I haven't had time
to really sit down and read and understand the manuals. From the various
features referred to in the Table of Contents of the 3 manuals (yes, 3. User
Reference, Technical Reference, and Graphic System User Manual (total: approx
140 pgs) it looks very impressive. The knowledge representation appears to be
primarily Semantic Net based, and there is support for graphically perusing
the network and building custom graphic objects for use with the system.
HOWEVER, after reading some of the User Reference manual, it quickly begins to
look like "you get what you pay for". E.g. the definitions of FORWARD
CHAINING and BACKWARD CHAINING in the User Reference are *REVERSED* (from what
I understand them to be):
"In general, facts make up evidence; in the process of determining the
validity of a fact, further evidence may be required. This propagation of
the thought process continues until a fundamental fact is encountered
which requires no further evidence. This fundamental fact is called an
ATOMIC FACT. And, this thought process is called FORWARD CHAINING.
Forward chaining is often used by human experts to validate assumptions.
On the other hand, if a given fact is being used to support the validity
of more than one fact, validating the other fact will often cause the
human expert to consider the other alternatives which it supports. This
thought process is called BACKWARD CHAINING."
Also, it becomes clear from the included price list that this is really only
designed to be a DEMO system. The KB is limited to 12K. For versions that
support larger KB's the price goes WAY up (e.g. 20K is $200, 100K is $1750,
1M is $9000). I also recently received a letter asking if I wanted to
subscribe to the monthly users newsletter and/or program updates (at $60/year
for the newsletter, $80/year for the updates, or $120/year for both).
For $20 (without the support) it will probably be OK for simple
prototypes.
Matt Heffron BEC.HEFFRON@USC-ECL.ARPA
Standard Disclaimer about these being my opinions, not those of my employer.
------------------------------
Date: 9 Feb 87 14:32:36 GMT
From: atux01!jlc@rutgers.rutgers.edu (J. Collymore)
Subject: A List of AI Books (for beginners)
I have received a number of requests for a posting any of my replies on my
query regarding good books on Artificial Intelligence for the beginner.
Well, here are those replies.
Thank you to all who responded to my query.
Jim Collymore
*******************************************************************************
Re: Need References to VERY BASIC Concepts of AI & Preferred Comp. Langs.
Artificial Intelligence, by Patrick Winston
===============================================================================
Re: Need References to VERY BASIC Concepts of AI & Preferred Comp. Langs.
Newsgroups: comp.ai,comp.misc
Organization: MIT Media Lab, Cambridge MA
The following two books are the most recommended ones I have seen and
are coordinated to introduce (1) concepts and (2) techniques of AI.
(1) Artificial Intelligence
Patrick Henry Winston
Addison Wesley
(2) Lisp
Patrick Henry Winston and Berthold Klaus Horn
Addison Wesley
As for good languages for AI, Lisp is good because with it you think
more about the solution than about the implementation and because it
allows you to develop the language you would have like to have to
solve the problem with in the first place. This latter requirement
seems to be important for the kind of approach used for AI these days.
There are two compilations of papers available which are of interest.
Titles are:
Readings in Artificial Intelligence
Readings in Knowledge Representation
I will try to get the publisher's name for you.
--Mario
===============================================================================
Subject: AI
Learn LISP and PROLOG.
Winston's or Steele's book on COMMON LISP are good. Steele is more of
a reference book.
Clocksin and Mellish is the default standard of PROLOG. However, Bratko
is easier to learn from. Bratko also provides a good intro to AI. I
highly recommend reading Bratko.
Winston's book on AI is TERRIBLE for a beginning book. For some history
MIT puts out some collected papers.
--------
LISP, Winston.
COMMON LISP, Steele.
Programming in Prolog, Clocksin and Mellish.
Programming in Prolog for Artificial Intelligence, Bratko.
===============================================================================
Subject: AI programming languages
Have you thought about trying Logo? This Department used it for years, though
we have now moved to Edinburgh Prolog.
Try reading Alan Bundy's book "Artificial Intelligence, an introductory
course", paperback published by Edinburgh University Press
------------------------------
Date: 10 Feb 87 17:40:17 GMT
From: jennifer!lyang@sun.com (Larry Yang)
Subject: Learing about AI (was Re: A List of AI Books (for beginners))
>Learn LISP and PROLOG.
When I took a class on Artificial Intelligence at Stanford (CS223, for
those who care), I figured I was ready. I knew PROLOG and LISP.
And I was all set to learn about this great thing called 'AI', at
the place where big names made it happen.
I was in for a surprise. Based on my experience, if you want
to learn about hard-core, theoretical artificial intelligence,
then you must have a strong (I mean STRONG) background in formal
logic. My understanding of PROLOG (which resembles predicate logic)
was very helpful, but it wasn't enough.
If you want to go out and build expert systems, or perform some other
intelligence engineering task, then PROLOG and LISP and a basic
grasp of logic are probably enough. But if you want to follow the
latest research (and maybe eventually do some of it), then a formal
training in logic is a must.
================================================================================
Whydoesn'titsnowintherightplaces?
--Larry Yang | *A REAL signature* _|> /\ |
lyang@sun.com,{backbone}!sun!lyang | "Limit? We don't | | | /-\ |-\ /-\
Sun Microsystems, Inc. | need no stinkin' <|_/ \_| \_/\_| |_\_|
Mountain View, California | 4-line limit! " _/ _/
------------------------------
Date: 9 Feb 87 11:06:42 est
From: Walter Hamscher <hamscher@ht.ai.mit.edu>
Subject: representation languages: richness and flexibility
Date: 5 Feb 87 03:37:30 GMT
From: berleant@sally.utexas.edu (Dan Berleant)
Hmm. I just attended a lecture in which frame based representation
schemes were touted on the basis of the fact that representation
languages should be rich and flexible.
Well, it sounds good, it even sounds simple, but I'm sure not sure what
it means! In the context of representation languages, what is
'rich', and what is 'flexible'?
Good question.
Flame on...
The term ``representation language'' is redundant. What other kind of
language could there be? Just think about languages, period, and the
terms make more sense. Languages are symbol structures that have an
interpreter. And since the terms are relative, it makes more sense to
ask ``what makes language A richer than language B'' and ``what makes
language X more flexible than language Y.''
Here's one way to characterize richness: A is richer than B if symbol
structures in A can finitely denote facts (i.e., the interpreter can
interpret as) that B can't. E.g., 1st order predicate calculus is
richer than propositional calculus because it has quantification,
which allows you to express infinitely large propositional
conjunctions and disjunctions. Frame languages, semantic nets, etc,
differ as to whether they correspond to first-, second-, or
omega-order logics, and that's probably the best way to characterize
its richness in a technical sense. If you replace finiteness with
compactness, it becomes more a matter of taste: frame languages print
nicely because they supress some redundancies, but does the computer
really care about that?
Here's one way to characterize flexibility: X is more flexible than Y
if a local incremental change to the denotation of a symbol structure
in X can be done by changing fewer symbols and relations. This
actually turns out to go along with richness sometimes. For example,
a frame based language with inheritance and cancellation is more
flexible than 1st order predicate calculus because (to beat on a tired
example) you can say that birds fly and then later say that penguins,
which are birds, don't fly, without having to go back and change the
original statement about how birds fly. You make a local addition and
you don't have to go around the whole symbol structure fixing a lot of
things up. What this goes to show is that a frame language with these
features has second order properties; if you go to 2nd order predicate
calculus via circumscription, you get this locality property back.
Now you get to the real question: what are the properties of the
interpreter that come packaged with the language? Does it give you
some kind of guarantee about completeness, about variant queries,
about constant time complexity for query answering, or what? Does the
language come with a basic set of facts about the world that you can
build on (like a subroutine library in a programming language)? Or
does it just stuff things into a database and let you figure out what
to do with them later? The richness and flexibility of the language
itself are not very interesting properties, it's the interpreter that
matters. What people usually mean when they say ``representation
language'' is ``belief language'', since they're talking about a
language whose purpose is to denote the beliefs of an agent. But if
you expect the interpreter of your belief language to do a lot of
automatic inferences that solve a significant part of the software
engineering problem for you, then you're probably expecting too much
from it: that's the job of a programming language and environment.
Flame off...
Walter Hamscher
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End of AIList Digest
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