Subject: About Artificial Intelligence

From: "Mr HG Kruger" <[email-masked]>
Date: Wed, 19 Feb 1997 12:43:21 SAST

Dear CCLers,

Below is my origional question.

I am trying to get background information on Artificial Intelligence. 
Review articles explaining a bit about the philosophy and 
principles as well as available software.

I will summarise the answers.
___________________________________________________

Thanks a lot for the people that responded.  Their message follow 
below:

Date sent:        Tue, 04 Feb 1997 10:17:47 +0000
From:             Thierry Hanser <[email-masked]>
Organization:     ICAMS
To:               Mr HG Kruger <[email-masked]>
Subject:          Re: CCL:About Artificial Intelligence

Hi,

   Well, here is a domaine specific introduction to AI in chemistry: 

	Application of Artificial Intelilligence in Chemistry H.M. Cartwright 
	Oxford Science Publications (Oxford Chemistry Primer)

-- 
===  Thierry Hanser        mailto:[email-masked]        ===
--       ICAMS             Tel : 0113 233 65 97 (work)           --
--   School of Chemistry   Home: 71 Wensley rd LS72LS Leeds (UK) --
=== University of Leeds    Tel : 0113 ??? ?? ?? (home)          ===

Date sent:        Tue, 4 Feb 1997 11:54:57 +0100
From:             [email-masked] (Niels van der Laag)
To:               [email-masked]
Subject:          Re: CCL:About Artificial Intelligence

Hi,

In my education we had a topic about Neural Networks/AI. I've two papers of it, however I'haven't read it for a long time. But it helped me started, so I think it will help you too.

Smits et.al, Chemometrics and Intelligent Laboratory Systems, 22 (1994) 165-189.
De Weijer et.al., Chemometrics and Intelligent Laboratory Systems, 16 (1992), 77-86.

The title of the first article (by Smits et.al) is:
"Using artificial neural networks for solving chemical problems, Part 1 Multi-layer feed-forward networks"

The title of the second is:
"Neural Networks used as soft-modelling technique for quantitave description of the relation between physical structure and mechanical properties of (ethylene terephtalate) yarns.

Furthermore the analytical chemistry department of our university works on neural networks applied to (analytical) chemical problems. I suggest to look at their WWW-pages (http://www-cac.sci.kun.nl/cac/).

I hope this is helpful.

Greetings

Niels 
 
+----------------------------------------------------------+
| Niels J. van der Laag                                    |
|      Student Computational Chemistry                     | 
|        Dept. of Physical Chemistry  (Solid State NMR)    |
|        University of Nijmegen                            |
|        Toernooiveld 1, NL-6525 ED Nijmegen, Netherlands  |
|        phone: ++31-24-3653112 email: [email-masked] |
|                                      [email-masked]   |
+----------------------------------------------------------+

Date sent:        Tue, 4 Feb 1997 10:00:08 -0500 (EST)
From:             Andy Dustman <[email-masked]>
To:               Mr HG Kruger <[email-masked]>
Subject:          Re: CCL:About Artificial Intelligence

On Tue, 4 Feb 1997, Mr HG Kruger wrote:

> I am trying to get background information on Artificial Intelligence. 
> Review articles explaining a bit about the philosophy and 
> principles as well as available software.

Here's what's on my bookshelf, though it may be out of date: 

Winston, P.H. "Artificial Intelligence, 2nd ed", 1984, Addison-Wesley.

Tanimoto, S.L. "The Elements of Artificial Intelligence", 1987, Computer Science Press.

Rumelhart, D.E., and McClelland, J.L. "Parallel Distributed Processing: Explorations into the Microstructure of Cognition. Vol 1: Foundations. Vol 2: Psychological and Biological Models", 1986, MIT Press.

The first two are probably the better ones for background information, though they will not be up-to-date on available software. 

--
Andy Dustman / Computational Center for Molecular Structure and Design / UGA
You can have my PGP public key by sending mail with subject "send file key".
You can have my PGP secret key when you pry it out of my cold, dead neurons.
http://charon.chem.uga.edu/~andy    mailto:[email-masked]    <}+++<

Date sent:        Wed, 5 Feb 1997 09:55:44 +0100
From:             "Edward S. Blurock" <[email-masked]>
To:               [email-masked]
Subject:          Re: CCL:About Artificial Intelligence

>>>>> HG Kruger writes:

Dear Gert:

There are many standard texts on AI.  One of the classic (but old) texts is 'Artificial Intelligence' by Nilsson.  In a certain sense the newer ones are based on these.  My AI course (see my homepage for links) is based on a combination of theory and p

As for AI software, the best collection of PD software is the CMU Artificial Intelligence Repository
http://www.cs.cmu.edu/Groups/AI/html/repository.html

It has collected just about everything (including doco)

hope this helps

blurock

Date sent:        Thu, 6 Feb 1997 18:54:45 +0100
From:             Frank Herrmann <[email-masked]>
To:               [email-masked]
Subject:          Re: CCL:About Artificial Intelligence

Dear Gert Kruger,

I don't know any review articles, but I am a bit used to AI from
studying computer science. The main areas in AI are:

expert systems (mycin - medical diagnosis)
knowledge representation (frames, scripts)
natural language understanding (computer linguistics, weizenbaums eliza)
cognitive science (society of mind:marvin minsky)
automated theorem proovers (logic programming, prolog)
computer chess
(neural networks and genetic algorithms)

I don't know specific software (depends on application area) but the
common programming languages are LISP,PROLOG and SMALLTALK. These
languages focus on symbolic computation instead of numeric
computation. Lisp programs can modify themselves since there is no
distinction between program and data (all lists). A key word is object
oriented programing (classes instances).

I think the philosophy of AI is to model human thinking and to see
information processing as symbol manipulation rather than number
crunching. A "typical" AI program is one where the programer cannot
predict its behavior or explain what it is doing right now. This is
because AI programming is more telling how to do things than what to
do. 

Small anekdote:

There is a project called psych or so. A program is fed with a lot of
everyday information and is supposed to develop a kind of "common
sense". It has been said that the program asked the "instructors"
surprising questions. Silly questions on the first glance, nobody
could explain how the program generated this question and from what
data. Only when the program had to explain why it is a question for it
than the instructors had to admit that it was logical (but still silly).

so much for AI in a nutshell

Regards,

-- 
---------------------------------------------------------------------
           Frank Herrmann, Computer Scientist, PhD Student            
   Institute of Parallel and Distributed High-Performance Systems    
                   (IPVR) University of Stuttgart                     
                      Breitwiesenstrasse 20-22                        
                    D-70565 Stuttgart  (Germany)                      
           Tel: (49) 711-7816-358, FAX: (49) 711-7816-250            
          email: [email-masked]         
http://www.informatik.uni-stuttgart.de/ipvr/bv/personen/herrmann.html

Date sent:        Fri, 7 Feb 1997 14:44:29 +0100
From:             Frank Herrmann <[email-masked]>
To:               [email-masked]
Subject:          Re: CCL:About Artificial Intelligence

Dear Gert,

This is what I understood:

You have one molecule and associated experimental data
(e.g. spectra). Then you look for other molecules in databases (also
associated spectra) that correlate best (kind of similarity).

To do this on a symbolic level, you have to extract features from the
continues spectra. Then you need rules that deduce a classification
from the features. If you can both extract features and rules than you
can use Prolog to proove the theorem that a molecule is of a specific
type.

Or you use neural nets to automatically (and implicitly) detect
features and rules. Then you need a training set of examples. You need
pairs of structures with a known correlation or similarity. After
training the net you can feed your target molecule and (one after
another) the database molecules to get a similarity value. 

Unfortunately, this is not very related to my work, so I cannot
provide you with literature or software hints. But if you want to try
neural nets, our departement developed the SNNS (Stuttgart neural net
simulator) that is publicly available. The homepage of my collegue
contains a link to download it:

http://www.informatik.uni-stuttgart.de/ipvr/bv/personen/mache.html

hope this helps,

Regards,

-- 
---------------------------------------------------------------------
           Frank Herrmann, Computer Scientist, PhD Student            
   Institute of Parallel and Distributed High-Performance Systems    
                   (IPVR) University of Stuttgart                     
                      Breitwiesenstrasse 20-22                        
                    D-70565 Stuttgart  (Germany)                      
           Tel: (49) 711-7816-358, FAX: (49) 711-7816-250            
          email: [email-masked]         
http://www.informatik.uni-stuttgart.de/ipvr/bv/personen/herrmann.html

Date sent:        Fri, 7 Feb 1997 14:44:29 +0100
From:             Frank Herrmann <[email-masked]>
To:               [email-masked]
Subject:          Re: CCL:About Artificial Intelligence

Dear Gert,

This is what I understood:

You have one molecule and associated experimental data
(e.g. spectra). Then you look for other molecules in databases (also
associated spectra) that correlate best (kind of similarity).

To do this on a symbolic level, you have to extract features from the
continues spectra. Then you need rules that deduce a classification
from the features. If you can both extract features and rules than you
can use Prolog to proove the theorem that a molecule is of a specific
type.

Or you use neural nets to automatically (and implicitly) detect
features and rules. Then you need a training set of examples. You need
pairs of structures with a known correlation or similarity. After
training the net you can feed your target molecule and (one after
another) the database molecules to get a similarity value. 

Unfortunately, this is not very related to my work, so I cannot
provide you with literature or software hints. But if you want to try
neural nets, our departement developed the SNNS (Stuttgart neural net
simulator) that is publicly available. The homepage of my collegue
contains a link to download it:

http://www.informatik.uni-stuttgart.de/ipvr/bv/personen/mache.html

hope this helps,

Regards,

-- 
---------------------------------------------------------------------
           Frank Herrmann, Computer Scientist, PhD Student            
   Institute of Parallel and Distributed High-Performance Systems    
                   (IPVR) University of Stuttgart                     
                      Breitwiesenstrasse 20-22                        
                    D-70565 Stuttgart  (Germany)                      
           Tel: (49) 711-7816-358, FAX: (49) 711-7816-250            
          email: [email-masked]         
http://www.informatik.uni-stuttgart.de/ipvr/bv/personen/herrmann.html

__________________________________________________________
Dr HG Kruger, Dept Chemistry, University of Natal,
PO Box 18091, Dalbridge 4014, Durban, South Afica
Tel  +27-31-2602181   Fax  +27-31-2603091
Email  [email-masked] 
__________________________________________________________