HDTenEightyP/NetNews huggingface dataset:
From: es_fuchs@rcvie.co.at (Daniel Fuchs)
Subject: Bibliography on fuzzy logic ?
Reply-To: es_fuchs@rcvie.co.at
Organization: Alcatel ELIN Research
Date: Fri, 28 Aug 1992 10:21:18 GMT

Hello !

Is anyone here aware of a bibliography on fuzzy logic available through FTP 
(or mail-server or any other way ...) ?

Any help would be greatly appreciated.

Thanks in advance.

--Daniel.

(Please reply through email: Daniel.Fuchs@rcvie.co.at)


Newsgroups: comp.ai
From: dieter@sun-3.cs.uct.ac.za (Dieter Polzin)
Subject: Help - GoldenLisp
Organization: Computer Science Department, University of Cape Town
X-Newsreader: Tin 1.1 PL5
Date: Mon, 31 Aug 1992 13:12:58 GMT

Sorry if this is in the wrong group, but a friend of mine
wants to know all about GoldenLisp.  Where to buy, how
much etc, etc, and features ...

If anyone can help please mail me below.  Ta.

P.S.  I stay in R.S.A.

--
| grant@sun-3.cs.uct.ac.za  Phone 650 4057, Fax 650 3726        |
\_______________________________________________________________/
From: benanav@earth.njit.edu (Dan Benanav)
Newsgroups: comp.ai
Subject: What has AI achieved?
Date: 2 Sep 92 09:09:04 GMT
Organization: New Jersey Institute of Technology, Newark, N.J.

I am trying to get an idea about the accomplishments in AI.
I am not interested in spin-offs rather I am interested in
such things as:

1)Vision recognition - Recognition of faces, chairs and everyday objects.

2)Speech and Handwriting Recognition.

3)Natural Language Understanding.

4)Automated Programming and theorem proving.

5)Automated Diagnosis and Design.

6)Overall the best robot?

7)Learning.

8)Any other relevant AI topic I didn't mention.

For each of these things I am interested in the most impressive
program (or two) and in general comments about success in these areas.
Please give a reference, especially for any outrageous claims.
Keep it short but to the point.  What if any is the most impressive
industrial application?  

For example in automated theorem proving the most impressive achievement
is the solving a some open problems using an automated theorem prover(ATP).
However those problems were rather esoteric.  In general, theorem provers
do not work well.   Theorem provers choke even on theorems that are
simple for us.  At the current time they have little practical value.


Please send in your answer only if you are reasonbly certain of your
claims.  References will be greatly appreciated.
You can email your responses to me and I will summarize.

I look forward to your responses!

---------------------------------------------------------------------
 Dan Benanav
 Assistant Professor.
 Computer and Information Sciences.
 New Jersey Institute of Technology.
From: lerman@sunny.teknowledge.com (Lee Erman)
Newsgroups: comp.ai
Subject: Re: AI Winter Refugees
Date: 31 Aug 92 17:12:59 GMT
Reply-To: lerman@teknowledge.com
Organization: Cimflex Teknowledge Corp., KSD
In-reply-to: ginsberg@t.Stanford.EDU's message of 25 Aug 92 16:29:54 GMT
Cc: ginsberg@t.Stanford.EDU
Bcc: lerman@teknowledge.com

In article <1992Aug25.162954.556@CSD-NewsHost.Stanford.EDU> ginsberg@t.Stanford.EDU (Matthew L. Ginsberg) writes:

   From: ginsberg@t.Stanford.EDU (Matthew L. Ginsberg)
   Newsgroups: comp.ai
   Date: 25 Aug 92 16:29:54 GMT
   References: <x+an!9a.vere@netcom.com>
   Organization: Computer Science Department, Stanford University

  [...]

   In the industrial sector, things are more complex because industrial
   jobs are somewhat more stable than academic ones.  Often individuals
   cannot be dismissed; entire corporations must disappear instead.
   Witness the demise of Teknowledge.

  [...]

						   Matt Ginsberg

Without engaging too deeply in this interesting thread, I would like to
correct a factual mistatement in that article:
	"Witness the demise of Teknowledge."

Teknowledge merged with American Cimflex Corp a few years back.  The
organization that was Teknowledge became the Knowledge Systems Division of the
new Cimflex Teknowledge Corp.  There are about of 35 of us here, many after 8
years or more (including me, Rick Hayes-Roth, Allan Terry, Bob London, Terry
Barnes, and Jim Davidson).  We are selling AI products (M.4 (nee M.1),
ProductTrack, Enterprise/DX), doing commercial AI work (GM, Motorola, etc),
and working on govt (DARPA and other) AI and architecture contracts (DSSA,
DICE, MMACE, Focus: HOPE, etc).  We've certainly not had the success of
Microsoft, but I wouldn't call it demise either.

I guess I can't resist an additional opinion:  Although many of the articles
in this thread put forth interesting hypotheses, almost all include no data to
back them up.  Here's a bit of data:  Of the "original 4" AI companies:
Teknowledge, Intellicorp, Inference, and Carnegie Group, ALL of them are still
around.

--Lee

	Lee D. Erman
	Cimflex Teknowledge Corp.	
	Knowledge Systems Division	
	1810 Embarcadero Road		phone:  415/424-0500 ext.422
	P.O. Box 10119			fax:    415/493-2645
	Palo Alto, CA 94303		email:  lerman@teknowledge.com
Newsgroups: comp.ai
From: finin@algol.cs.umbc.edu (Timothy Finin)
Subject: Models Of Diagnostic Inferences
Organization: Computer Science, University of Maryland, Baltimore County
				   
		   GRADUATE AI COURSE ANNOUNCEMENT
Distribution: md,dc
Date: Wed, 2 Sep 1992 20:33:37 GMT

		     Computer Science Department
	       University of Maryland Baltimore County
			     Baltimore MD
				   
	      CMSC 691: MODELS OF DIAGNOSTIC INFERENCES
				   
		    Fall, 1992, TuTh 4:00 - 5:15pm


Instructor          Yun Peng
  Phone:          (301)455-3816
  Office:         TF II, room 105
  Email:          ypeng@algol.cs.umbc.edu

Text
  Y. Peng & J.A. Reggia, "Abductive Inference  Models for
  Diagnostic Problem-Solving", Springer-Verlag, 1990, and
  supplementary journal papers.

Course Description
  This course offers an introduction and in depth discus-
  sion  of  important  issues in the area of abduction, a
  prominent but less studied type of inference, and diag-
  nostic reasoning, a typical representative of abductive
  inference. Topics include the nature and  the  applica-
  tions of abduction; knowledge representation and infer-
  ence paradigms for  diagnostic  reasoning;  formal  and
  heuristic  methods for uncertainty in causal reasoning,
  etc.  Several state-of-the-art AI  and  neural  netwrok
  models for diagnostic problem-solving will also be dis-
  cussed.

Major Topics To Be Covered
  1. Abduction vs deduction  and  induction,  its  unique
     characteristics, and its applications.
  2. Different representations for causal  knowledge  and
     the characteristics of causal inference.
  3. Uncertainty in causal reasoning  and  representative
     approaches to this problem.
  4. Acquisition of causal knowledge (learning).

Theoretical Models To Be Discussed
  1. Shortliffe et al: MYCIN
  2. Peng & Reggia: Parsimonious Covering Theory and Pro-
     babilistic Causal Model
  3. Reiter: Theory of Diagnosis From the First Principle
  4. De Kleer: Truth Maintenance System and  the  General
     Diagnostic Engine
  5. Pearl: Bayesian Network
  6. Dempster-Shafer theory
  7. Neural networks: Backpropagation  networks;  Hebbian
     learning method

Work Load
  One exam (50%), one paper/project (50%).

Background
  Necessary: Basics of AI, elementary set theory and probability theory.
  Desirable: Basic knowledge of neural networks.
Newsgroups: comp.ai
From: roberto@erg.sri.com (Roberto Desimone)
Subject: Re: AI Winter Refugees
Organization: SRI International, Menlo Park, CA
Date: Tue, 25 Aug 92 16:37:04 GMT

In article <x+an!9a.vere@netcom.com> vere@netcom.com (Steven Vere) writes:
>
>o what were the causes of AI Winter?
>o why has AI gone out of fashion in the US industrial sector?

I'm not sure that this is true. Certainly there is a greater trend for
larger US companies to reduce the amount of fundamental AI research
they are pursuing, and, instead, pursue more applied (shorter-term) AI
research and development. 
>
>   Countervaling views  are welcome, but please be prepared to explain
>the following phenomena: near-zero employment opportunities advertised
>in AI Magazine and similar forums; near-zero recruiting at recent AAAI
>conferences; major  declines   in  attendance at AAAI   over the  past
>several years.

Possible (partial) explanations for these phenomena are that there are
many more people around with AI experience (albeit at widely varying levels 
of expertize), and so considerably more people than before going for a 
slightly larger  number of jobs. Whereas in the past (only a few years ago) 
companies had to advertize to find good people to fill their staff positions, 
now they are getting plenty of good resumes and don't feel they have to 
advertize so much. Likewise for recruiting at AAAI.

Regarding attendance at AAAIs. Again there are many more AI conferences now.
For applied AI, here are a few that are well attended:

 IAAI    -   Innovative Applications of AI
 CAIA    -   Conf. on AI Applications  (IEEE)
 IEA-AIE -   Industrial and Engineering Applications of AI & Expert Systems

Then there are the more specific (sub-field) conferences and workshops.

I'm not stating that the above totally explains the phenomena you describe.
I'm sure that some companies are thinking twice about expanding their AI
staff, others are even shrinking. But, on the whole, I don't see a widespread
reduction in AI staff. I tend to keep in touch with the more applied side of
AI, rather than the pure research end of AI, so maybe I'm getting a biased
view.



-- 
Roberto Desimone      roberto@erg.sri.com          Tel: (415) 859-4038
SRI International (EK335), 333 Ravenswood Avenue, Menlo Park, CA 94025
Newsgroups: comp.ai
From: rsun@athos.cs.ua.edu (Ron Sun)
Subject: Re: AI Winter
Organization: University of Alabama Department of Computer Science
Date: Fri, 28 Aug 1992 19:17:29 GMT

In article <1992Aug28.125825.7628@csc.ti.com> thrift@ra.csc.ti.com (Phil Thrift) writes:
>knowledge-based approaches are declining, learning-based approaches
>are on the upswing (see the latest addition of Winston's AI text).
>The basic idea is that in organizations there's lots of undigested
>data but litle knowledge in many important problem domains (the push
>for knowledge-based AI came from the idea that there's lots of human
>knowledge in organizations that we could computerize).  Whether
>machine learning, data mining, neural nets, statistical modeling or
>whatever the hell you want to call it comes to the rescue -- we shall

Although interesting, this thing is no longer AI: it has nothing to do
with the original grand goal of understanding intelligence and cognition.
I hope that AI can be a discipline that has its own  consistent program
and stands on its own merit (a minimal requirement for any discipline,
I gusee), rather than being whatever "AI people" are doing.

--Ron
From: cam@castle.ed.ac.uk (Chris Malcolm)
Newsgroups: comp.ai
Subject: Re: What has AI achieved?
Date: 2 Sep 92 15:13:48 GMT
Organization: Edinburgh University

In article <benanav.715424944@earth.njit.edu> benanav@earth.njit.edu (Dan Benanav) writes:

>I am trying to get an idea about the accomplishments in AI.

>6)Overall the best robot?

"Overall" is just a way of pretending you're not making assumptions or
preferences. For example:-

	What is overall the best animal? 

	Er .. the Lion!

	But lions can't fly!

Etc..
-- 
Chris Malcolm    cam@uk.ac.ed.aifh          +44 (0)31 650 3085
Department of Artificial Intelligence,    Edinburgh University
5 Forrest Hill, Edinburgh, EH1 2QL, UK                DoD #205
From: sonix@schunix.uucp (Duane Morin)
Newsgroups: comp.ai
Subject: "Mini" Neural Net Application: Ref's?
Date: 28 Aug 92 20:29:42 GMT
Organization: SCHUNIX Public Access Unix for Worcester County, Massachusetts, USA
X-Newsreader: Tin 1.1 PL4

Ok, so we have an application at work that I think would benefit from the 
application of a simplified neural net approach (I get this train of thought 
from the C Users Journal, Sept 1992, article of the same theme).  Here's
what I figure:

	We have an application that calculates a "baseline" of 12 points over
	a series of increasing frequencies.  This data is then graphed, and 
	it is up to the user to decide whether or not the trace is acceptable,
	according to the general shape of the trace drawn.  The pictures that
	the user sees will be fairly clear:  OK trace, Input Cable Not 
	Working (flat line), Object in Path (Crooked line), etc...  *BUT*
	each trace will be different, i.e. two OK traces will not be IDENTICAL
	but will look basically the same.

Now, the question.  Does anyone more familiar with the topic than I think 
that we can implement something to analyze these traces and predict what 
class they fall in, without having to go to the user?  Of course, the user 
could be consulted as a last ditch effort, i.e. if the program could not 
recognize the trace as belonging to a particular class.  

I was thinking that maybe we could take several examples of each kind of 
trace and "train" the computer to recognize them.  That way, when the user 
creates a trace of a particular class, the computer would be able to 
distinguish between the various (say, 10 ) classes of traces.  

Is this possible?  Feasible?  Thanks for any responses.  Email preferred,
summary to the net if there's interest. 

Thank you,
 Duane Morin
 Software Development Manager
 Walker Sonix
 Worcester, MA 01604
 sonix@schunix.uucp
Newsgroups: comp.ai,alt.hypertext,comp.ai.edu
From: mm@vigsun1.larc.nasa.gov (Mala Mehrotra)
Subject: Number of Expert Systems currently in use
Organization: /home/vigyan/mm/.organization
Distribution: comp,alt
Date: Wed, 2 Sep 1992 17:31:09 GMT

I need information on number of expert systems currently in use. Does anyone
have any numbers or references which can provide me with this information?
Please reply via e-mail to mm@vigsun1.larc.nasa.gov. Thanx in advance!

Mala Mehrotra
From: reiter@eagle.ed.ac.uk (Ehud Reiter)
Newsgroups: comp.ai
Subject: Re: AI Winter Refugees
Date: 28 Aug 92 10:38:04 GMT
Reply-To: e.reiter@ed.ac.uk (Ehud Reiter)
Organization: Dept AI, Edinburgh University, Scotland

With regard to the AI Winter debate, one thing that really bothers me about
AI is the level of academic dishonesty, i.e., situations where someone gets
a large research grant to do something which he or she knows is impossible,
on the theory that `money is money, and once I've got it I can put it to
good use'.  Anyone in the physics community who submitted a grant to build a
perpetual motion machine would be laughed at, but similar things in AI seem
to happen all the time, and my fear is that this will lead the outside world
to regard us as a bunch of charlatans, who don't deserve any funding at all.
--
						Ehud Reiter
						(e.reiter@edinburgh.ac.uk)
Newsgroups: comp.ai
Newsgroups: comp.ai
From: vinay@cs.toronto.edu ("Vinay K. Chaudhri")
Subject: CYC as a test system
Organization: Department of Computer Science, University of Toronto
Distribution: na
Date: 4 Sep 92 16:25:53 GMT

I heard that the CYC group is giving out test copies of their
knowledge base to interested research groups.  Has anyone 
tried this?  What is the procedure for obtaining CYC ?

thanks,
vinay.
From: tallis@starbase.mitre.org (Hans Tallis)
Newsgroups: comp.ai
Subject: Toward Computational Behavior?
Date: 27 Aug 92 14:12:04 GMT
Organization: The MITRE Corporation

I'm looking for references or thoughts on computational specification of
"behaviors".  For example, given the two behaviors

	- when you see food, turn left
	- when you smell food, turn right

I need to compute things like the union and intersection of these behaviors.  I
also need to talk about more and less general behaviors (some partial ordering
would be fine), perhaps the complexity of a behavior, and test whether two
behaviors are the same.  Actually I also am undecided about what exactly a
"behavior" is; it could be any of the following:

	- stimulus/response table 
	- goal to achieve
	- sequence of motions
	- collection of beliefs
	- your-favorite-here

I haven't see many comprehensive discussions about behaviors that would lead to
a widely agreed-upon formalization of them.  So I'm looking for pointers.

Thanks,						--Hans Tallis
						tallis@mitre.org
Newsgroups: comp.ai
From: will@kafka.SAIC.COM (Will Scarvie x6388)
Newsgroups: comp.ai
Subject: Strategy engine and representing game rules
Date: 28 Aug 92 18:05:01 GMT

Hello everyone,

  I'm thinking about beginning a project which would create a strategy engine
capable of reading a database of rules and creating a space of possible
strategies based on those rules.  The rule database would consist of the
rules of some fairly simple baord wargame translated into a yet-to-be-defined
machine readable language.  The idea here is to create a language capable of
expressing a large number of different wargame rules sets, and an engine
capable of reading and operating on any rules set expressed in that language.
The goal of the strategy engine would be to propose strategies for the
playing of a game based on current board position, victory conditions, and
whatever else is relevant.

  But, I'm in need of some pointers on where to look for work already done in
this area.  Particularly interesting would be references which deal with
representing boards, units and rules in a program, and anything done already
on decision making and planning in games.  I have a feeling that a lot of work
has been done in similar areas for the creation of computer controlled players
but I don't know where to look.

  If any of you have ANY ideas about what I ought to be reading for this
project, please forward them to me.  If there's any interests, I'll compile
a listing of the resources and post them to this group.

Thanks very much,

Will Scarvie
will@kafka.saic.com

DISCLAIMER:
The opinions expressed are entirely my own and should not be construed to 
represent those of my company.
From: wiley@cactus.org (Mark S. Wiley)
Newsgroups: comp.ai
Subject: AI Expert Magazine, any good?
Date: 4 Sep 92 14:39:32 GMT
Article-I.D.: cactus.1992Sep4.143932.419
Distribution: na
Organization: Capital Area Central Texas UNIX Society, Austin, Tx

I want to learn more about what's currently going on in AI, is AI Expert
magazine a good source?
-- 
Mark S. Wiley
mark.wiley@cactus.org
From: christb@infmuc (Christian Barmala)
Newsgroups: comp.ai
Subject: novice question
Date: 4 Sep 92 11:36:13 GMT
Organization: Informix Software, Inc.
X-Newsreader: Tin 1.1 PL3

Can anyone tell me the difference between
-	Facts,
-	Rules and
-	Knowledge?

I think a fact is for instance:
	Peter is Johns brother

a rule is:
	if A is B's brother and B is C's brother then A is C's Brother

Knowledge is:
	Transitivity means: If rule R applys to (A,B) and (B,C) then
	it applies also to (A,C)
	is_brother_of, is_boss_of, consists_of, ... are transitive rules.

Am I right?
Newsgroups: comp.ai,comp.ai.edu,comp.ai.shells
From: rthien@c3po.rdt.monash.edu.au (rj thien)
Subject: Require Articles/Papers on CLIPS Knowledge-Processing Speed vs. Other ES Shells.
Organization: Monash University, Melb., Australia.
Date: Thu, 27 Aug 1992 22:45:27 GMT

Hi there Netters,

I was wondering if anyone could point me to any journals or magazines
that review a spread of different Expert System Development tools
and shells, comparing their knowledge-processing speed and capability.

I am particularly interested in the comparison between NASA's CLIPS
and Neuron Data's Nexpert Object.

Any help is welcomed.  Please reply by email.  Thank You.

Cheerio,

	Roger....


| Roger J. Thien, Department of Robotics and Digital Technology      
| Monash University (Caulfield Campus), P.O. Box 197, Caulfield East,
| VIC 3145, AUSTRALIA.  Email: rthien@monu6.cc.monash.edu.au,       
| rthien@rossum.rdt.monash.edu.au, rthien@daneel.rdt.monash.edu.au 
-- 
| Roger J. Thien, Department of Robotics and Digital Technology      
| Monash University (Caulfield Campus), P.O. Box 197, Caulfield East,
| VIC 3145, AUSTRALIA.  Email: rthien@monu6.cc.monash.edu.au,       
| rthien@rossum.rdt.monash.edu.au, rthien@daneel.rdt.monash.edu.au 
Newsgroups: comp.ai
From: kaykit@iti.gov.sg (Chan Kay Kit)
Subject: Information on commercial OCR systems needed
Reply-To: kaykit@iti.gov.sg
Organization: Information Technology Institute, National Computer Board, S'pore
Date: Mon, 31 Aug 1992 05:26:40 GMT

Hello everyone,

	There are many commercial OCR engines on the market right now for
reading printed text. I am aware of products like the Kurzweil K-5000 and
Discover 7320 Model 5 (from Xerox), OmniPage (from Caere Corp.), SAIC (from
Differential Processing System) and NestorReader.

	I wonder if anybody has done independent evaluation of any OCR
products, since such information would be useful to potential users. 
Specifically, I need answers to the following questions:


(i) name of product and company which produces it  

(ii) fonts and point sizes handled by product

(iii) whether the OCR engine exhibits true multifont and "multisize"
      capability i.e. is able to recognize text without the user
      having to specify in advance its font type and point size.

      If so, what is the accuracy over a variety of fonts and point sizes?

      If not, what is the accuracy over common fonts like Courier, Times
      Roman and Helvetica and point sizes from 8 to 24 points? 

(iv) What are the commercial OCR products, if any, that can
     1. recognise a page of text containing a mixture of fonts and point
	sizes at greater than 98% accuracy?

     2. recognise a page of text containing a single known font and a
	range of point sizes at greater than 99% accuracy?

(v) Are there any commercial OCR systems that accept noisy input or 
    text on shaded background eg. carbon and photostated/fax copies and
    still maintain an acceptable accuracy (say greater than 95%)?
  

Further, if there are any evaluations of commercial handwritten OCR systems, 
I will also be interested to hear about them.

Please email all replies/references to kaykit@iti.gov.sg. If there are 
enough responses, I will summarise.

Thanks!





---
Kay-Kit CHAN    			| Internet: kaykit@iti.gov.sg
Knowledge Systems Lab			| Bitnet:   kaykit@itivax
Information Technology Institute	| Tel:      (65) 772-0920
National Computer Board of Singapore    | Fax:      (65) 770-3043

Newsgroups: comp.ai,alt.hypertext,comp.ai.edu
From: ginsberg@t.Stanford.EDU (Matthew L. Ginsberg)
Subject: Re: Number of Expert Systems currently in use
Organization: Computer Science Department, Stanford University
Distribution: comp,alt
Date: Wed, 2 Sep 1992 16:58:22 GMT

In article <MM.92Sep2123109@vigsun1.larc.nasa.gov> mm@vigsun1.larc.nasa.gov (Mala Mehrotra) writes:
>I need information on number of expert systems currently in use. Does anyone
>have any numbers or references which can provide me with this information?
>Please reply via e-mail to mm@vigsun1.larc.nasa.gov. Thanx in advance!

It depends greatly on whom you ask (and what you call an expert system).
Here is an excerpt from my upcoming Intro AI text:

   How many successfully fielded expert systems are there?  It depends on
   whom you ask.  Of the three that we have mentioned, R1 and the
   oil-well tool were successfully delivered to their corporate sponsors;
   MYCIN has not been put into actual use.  Ed Feigenbaum will tell you
   that there are on the order of 5000 successfully fielded expert
   systems; Hubert Dreyfus, one of AI's leading critics, has said that
   there are about five fielded systems, and only four of those make
   money.  A decade ago, \cite{Feigenbaum:fifth} listed forty-three
   ``selected experimental and operational expert systems,'' of which six
   were described as operational.  Two of these six were medical systems
   that have, to the best of my knowledge, never been used clinically.

I, too, would appreciate hearing about any definitive answer.  Thanks!

						Matt Ginsberg