HDTenEightyP/NetNews huggingface dataset:
From: hulthage@morue.usc.edu (Ingemar Hulthage)
Newsgroups: comp.ai
Subject: IJCAI reviewing
Date: 21 Jan 1993 10:41:03 -0800
Organization: University of Southern California, Los Angeles, CA
Distribution: world
NNTP-Posting-Host: morue.usc.edu


Scott E. Fahlman (sef+@cs.cmu.edu) writes, apropos fairness as a
guarantee for quality:

	Fairness is no guarantee at all.  Refereeing would be totally
	fair if the referees were denied access to the paper itself,
	as well as the author's identity and the abstract.  But
	quality control would suffer.

	In my opinion, Brian Gaines is right.  There probably is some
	personal bias in refereeing, but we can't effectively hide the
	identities of authors.  Even if we could, it would be foolish
	to deny referees some of the information they need in order to
	assess the value and reliability of the paper, just to
	eliminate some *possible* bias.  Pseudo-blind refereeing does
	far more harm than good.

I don't understand why the fact that anonymity isn't a hundred percent
assured, is a compelling argument against attempting to assure
fairness.  I reviewed eight papers for IJCAI-93 and I couldn't
positively determine the authorship of any of them.  One paper
reported on results in the context of a project at a well known
computer science department and another contained an explicit
reference to previous work.  (I have to admit that I couldn't resist
the temptation to look at the references and give some thought to who
the authors might be.)  Still, the set of authors on the new paper
might not be identical with the authors of previous papers.  For all I
know, the main author may have been a new collaborator, postdoc,
graduate student etc.


Ingemar Hulthage

Associate Professor
Computational Organization Design
University of Southern California
Los Angeles, CA 90089-0021
(213) 740-4044 (voice)
(213) 740-8771 (Administrative assistant & fax)
From: kd@doc.ic.ac.uk (Kostis Dryllerakis)
Newsgroups: comp.ai
Subject: Re: Prolog under MS-Windows
Date: 22 Jan 1993 12:15:25 GMT
Organization: Imperial College, London, U.K.
Distribution: world
NNTP-Posting-Host: confucius.doc.ic.ac.uk


In article <123940001@hpindda.cup.hp.com>, suji@hpindda.cup.hp.com (Sujatha Bodapati) writes:
> I am looking for a prolog compiler that will run on the PC, under MSWindows
> and provides a Microsoft C interace. Ideally, I would like to run the 
> prolog program as a DLL.
> Does anyone know of such a compiler.
> Any pointers/suggestions/recommendations would be greatly appeciated. 
> 	       Thanks in advance.
> 				       Sujatha
> 
> PS: This information is needed quite urgently
> 
	You can try LPA Prolog for Windows. It is a fairly good environment and offers
	compilation, easy window handling and DLL as well. 
	LPA is a commercial program and should cost between 400-1000 pounds here in england

Kostis

-------------------------------------------------------------------------------
K J Dryllerakis					Deparment    of    Computing
Logic Programming Group				Imperial  College,    LONDON
Janet: kd @uk.ac.ic.doc				Tel:  +44(71) 589 5111 x5089
Uucp : kd@icdoc.UUCP				Internet:    kd@doc.ic.ac.uk
-------------------------------------------------------------------------------
Newsgroups: comp.ai,rec.arts.books,misc.writing,rec.arts.int-fiction
From: kasprj@nuge105.its.rpi.edu (Jim Kasprzak)
Subject: Re: Computer writes a book?
Reply-To: kasprj@rpi.edu
Organization: The Big Wedge 
Date: Wed, 27 Jan 1993 15:20:51 GMT

In article <1k56lgINNc5q@shelley.u.washington.edu>, twain@milton.u.washington.edu (Barbara Hlavin) writes:

|> What I want to know is, why does anyone *want* a computer to write a book? 
 
 I think you've missed my point here. As I mentioned in a line of my post
which you deleted, Prof. Bringsjord is primarily concerned with the problems
in philosophy and computing which one faces when trying to get a computer to
write a book. Questions like:
 
 - What sort of data base does one need to work from to write a story?
 - What is the difference between a story and a narration?
 - Is it possible for a computer to use the subtleties of metaphor and 
   imagery inherent in language?
 - How does the "programming" of an author, whether human or computer, 
   affect what the author writes?
 
 This is just the beginning. I am not involved in Prof. Bringsjord's work;
I only learned about it through a talk he gave to the local chapter of the
ACM. Perhaps I should tell him that the good folks on Usenet have taken an
interest in his work, and let him speak for himself.

|> Professor Bringsjord should be working on a project aimed at getting 
|> a warm-blooded human being to produce meaningful stories. 
 
 He is. He's written a novel titled _Soft Wars_, and is currently trying to
get working on a second novel.
------------------------------------------------------------------
     __  Live from Capitaland, heart of the Empire State...
 ___/  | Jim Kasprzak, computer operator @ RPI, Troy, NY, USA
/____ *| "And the men who hold high places must be the ones who start 
     \_|      To mold a new reality -- closer to the heart."
      ====  e-mail: kasprj@rpi.edu or kasprzak@mts.rpi.edu
From: liny@clu.cf.ac.uk (Yuen Lin)
Newsgroups: comp.ai
Subject: Re: Natural Language newsgroup
Date: 26 Jan 93 11:24:01 GMT
Organization: Computational Linguistics Unit, U of Wales - Cardiff, Cardiff, UK
X-Mailer: Cardiff Computing Maths PP Mail Open News Gateway

I say 'yes'. We need this newsgroup.

Yuen
From: gh@cs.toronto.edu (Graeme Hirst)
Newsgroups: comp.ai
Subject: Re: English vs contents (was Re: New Problems in IJCAI Reviewing)
Date: 24 Jan 93 15:57:15 GMT
Organization: Oscar's Otter Emporium

In article <C19qD9.Dn7@cs.uiuc.edu> yang@milton.cs.uiuc.edu (Der-Shung Yang) writes:
>Just curious, did you reject that paper mainly becuase of some consistent
>English errors or because of some other reasons?  Also, what will you do
>if you review a paper that is excellent in contents but has some English
>errors here and there (assuming that those English errors do not cause
>any misunderstanding or unclearness)?

Assuming that the paper is comprehensible, then the acceptance decision
should be based on the content -- and will be, if I referee it.  But a
referee who, trying to be helpful, also points out errors in the English
does expect the author to correct those errors -- especially in the case
of conference papers, which, unlike journal papers, will not be polished
by a publisher's copy-editor.


-- 
\\\\   Graeme Hirst    University of Toronto	Computer Science Department
////   gh@cs.utoronto.ca  /  gh@cs.toronto.edu  /  416-978-8747
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: comp.ai
Subject: Re: Pattern Recognition
Date: 27 Jan 93 13:28:47 GMT
Distribution: comp
Organization: University of Pittsburgh


Yes, Schalkoff's book is one of 2 that I e   received recommendations
on. The other was Duda and Hart, which is supposedly coming a
out with a new edition within the year (?).
Newsgroups: comp.ai.genetic
From: danwell@IASTATE.EDU (Daniel A Ashlock)
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Reply-To: danwell@IASTATE.EDU (Daniel A Ashlock)
Organization: Iowa State University
Date: Thu, 21 Jan 1993 19:08:00 GMT

In article <1jlmb2INNjp4@gaia.ucs.orst.edu>, choup@ava.bcc.orst.edu (Ping Chou)
writes:
> 
>         Or the development of genetic algorithms has completed?
> 
>         Isn't it true that selection/mating/mutation/chromosome/hybrid/
> genotype/phenotype/gene/... are taken from genetics?

   The words yes, but genetic algorithms go far beyond the biological genetic 
model.  There are a number of papers on Lamarckian genetic algorithms, for
example.  Lamarck is a discarded reject in genetic theory; in genetic algorithms
his ideas are useful.

   I suppose what I'm getting at here is the point Chris Langston makes when he
attempts to difine the field of Artificial Life.  He says Alife is an attempt
to study life as it can be rather than as it is.  The practical code-hacking
implications of this are that while biology has benn and will continue to
be a source of insparation one cannot let it be a strait jacket.

>         Don't you think you may be able to discover new algorithms from
> studying genetics?

   Well, biology in general actually, but yes I'd guesstimate a roughly 100%
chance that there are good ideas for genetic algorithms lurking in biology. 
The paper I mentioned last week (Iterated Prisoner's Dillemma with Choice and
Refusal) studies the effect of rational partner selection on a genetic
algorithm.  Our inspiration for this was mate selection behavior in biology.
The effect was startling - the algorithm converged to places it never went
before.

   The problem is that taking a pile of courses won't help.  I've taken courses
and they consist of irrelevant trivia and foundational material valuable to work
within the discipline.  They seldom give the gestalt one needs to rip off good
ideas.  I maintain reading Science and Science news is a better way to get the
cross specialization you need to be a better GA programmer.
 
>         Do you really believe the complete genome of HIV is just a 4-state
> bits encoded string?

   Since there are at least thousands of HIV pseudospieceis, no.  On the
other hand the hardware we evolve our stings on, like my DEC station 3100,
is considerably less sophistocated than the hardware an HIV virus runs on,
a human cell.  The biology used to "run" a virus is roughly totally unrelated 
to the hardware used to run a GA.
 
>         Then please, give youself a chance, take a look at any modern
> genetics text book, and be a cross-discipline expert.

   You seem confused as to the source of knowledge...   ...the very best thing
you can do is ask an expert a specific question and ask questions while he
answers until you understand.  Reading Science will prevent you from being so
ignorant that said expert will refuse to talk to you.

> --Ping
> 
>         BTW, I would appreciate any reasonable arguments because I'm trying
> to learn ai-genetic from the genetics side.

   Good luck.

Dan
Danwell@IASTATE.EDU

Newsgroups: comp.ai.genetic
From: csg068@cch.coventry.ac.uk (Decker)
Subject: Re: Gassy GA package
Organization: Coventry University
Distribution: comp
Date: Mon, 25 Jan 1993 11:11:23 GMT

In article <TERRY.93Jan20201750@nambe.santafe.edu> terry@nambe.santafe.edu (Terry Jones) writes:
>
>The README file posted the other day for the Gassy GA package gave an
>outdated internet site - one that can no longer be reached.
>

Could some kind soul PLEASE mail it to me, as this WAS going to be a    
fundemental part of my honours project. HEEEEEEEEEEEEEEEEEELP!

Thanks in advance,

Mart
-- 
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 : DECKER : csg068@uk.ac.coventry.cch : Dept. of Computer Science, Cov Uni  :  
 : "Barriers crumble, empires fall. The Karmadrome's inside us all" - PWEI  :
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
From: pja@neuron.cis.ohio-state.edu (Peter J Angeline)
Newsgroups: comp.ai.genetic
Subject: Re: GA versus Simulated Annealing
Date: 21 Jan 1993 22:46:54 -0500
Organization: Ohio State Computer Science
	<1993Jan21.172459.27732@cs.ucf.edu>
Reply-To: pja@cis.ohio-state.edu
NNTP-Posting-Host: neuron.cis.ohio-state.edu
In-reply-to: long@next1.acme.ucf.edu's message of Thu, 21 Jan 1993 17:24:59 GMT

In article <1993Jan21.172459.27732@cs.ucf.edu> long@next1.acme.ucf.edu (Richard Long) writes:

   From the few cases that I've read about the successes of GA,  
   crossover is particularly good for a problem that has a natural  
   hierarchical structure to the optimal solution, since crossover exploits  
   local fitness maxima in the construction of a global maximum.  

Not quite right.  The _building block hypothesis_ states that GAs work well
when the best solutions are _compositional_ rather than hierarchical.  By
compositional I mean that smaller pieces of the solution are pretty good but
when you get a few of the pretty good smaller pieces together then the result
is something better than all of the smaller pieces. This tops out when all of
the small pieces are together giveing a maximal fitness.

   --
   Richard Long
   Institute for Simulation and Training
   University of Central Florida
   12424 Research Parkway, Suite 300, Orlando, FL 32826
   (407)658-5026, FAX: (407)658-5059
   long@acme.ucf.edu

-pete angeline

-------------------------------------------------------------------------------
Peter J. Angeline            ! Laboratory for AI Research (LAIR)
Graduate Research Assistant  ! THE Ohio State University, Columbus, Ohio 43210
ARPA: pja@cis.ohio-state.edu ! "Nature is more ingenious than we are."

-- 
-------------------------------------------------------------------------------
Peter J. Angeline            ! Laboratory for AI Research (LAIR)
Graduate Research Assistant  ! THE Ohio State University, Columbus, Ohio 43210
ARPA: pja@cis.ohio-state.edu ! "Nature is more ingenious than we are."
From: thrift@ra.csc.ti.com (Phil Thrift)
Newsgroups: comp.ai.genetic
Subject: Re: Genetic algorithm question
Date: 24 Jan 93 15:30:52 GMT
Organization: TI Central Research Laboratories, Dallas

In article <1993Jan22.213851.8114@wuecl.wustl.edu> 
liu@rodin.wustl.edu (Liu Ann Pheng) writes:
   Greetings!  I have a question for the net readers:
   What is the relationship (as well as main differences, if any) between
   genetic algorithm and evolutionary programming?  
   Thanks,
   Liu, Ann Pheng

I've seen the terms "genetic algorithms" (GAs), "evolutionary programming",
(EP), "genetic programming" (GP), and "evolutionary strategies" (ESs).
There are probably some others. The main difference it seems to me is 
whether the method includes recombination (crossover). For example

               EP is GAs without sex.

-Phil

ps. I am obviously using the genetic usage of the word "sex".
From: andersen@s1.elec.uq.oz.au (Hans Andersen)
Subject: Reply: So, genetic algorithms have nothing to do with genetics?
Reply-To: andersen@elec.uq.oz.au
Organization: Dept. of Elec. and Comp. Eng. 
Date: Mon, 25 Jan 1993 01:18:24 GMT

choup@ava.bcc.orst.edu (Ping Chou) writes:
>
>        Or the development of genetic algorithms has completed?
>
>        Isn't it true that selection/mating/mutation/chromosome/hybrid/
>genotype/phenotype/gene/... are taken from genetics?
>
>        Don't you think you may be able to discover new algorithms from
>studying genetics?
>
>        Do you really believe the complete genome of HIV is just a 4-state
>bits encoded string?
>
>        Then please, give youself a chance, take a look at any modern
>genetics text book, and be a cross-discipline expert.
>
>--Ping
>
>        BTW, I would appreciate any reasonable arguments because I'm trying
>to learn ai-genetic from the genetics side.


Dear Ping and anyone else who agrees with him,

I would like to agree with some of the other responses I have read and
call you arrogant, but I suspect that you probably get some strange
sort of pleasure from this so I won't (except that I already did -
Oh well). 

Anyway what I wanted to say was that I think you are being misled by
the name "Genetic Algorithms" which I consider not to be a very good
one (am I being arrogant now?).  Genetics is only one place where
evolution is visible.  It happens to be one which is perhaps easier
to analyse in a scientific way than others, but nevertheless if you
restrict your thoughts to this field only you are shooting yourself
in the foot.

The genetic model for what could be called "Artificial Evolution"
imposes unnecessary restrictions on development.  If you were to argue 
that it doesn't you'd be arguing that no operation exists which could be 
useful yet is not genetically viable.  At the same time you'd also be
contradicting yourself since evolution is all about trying new things
and letting variety prosper, and hence you'd be restricting your creativity
because of some gospel-like belief that genetics is the true model for
artificial evolution.

Can I suggest that you think of the chromosome (as termed in GAs), not as 
a chromosome but as a representation of an entity, and that this entity
can be anything which can evolve, be it an economic theory, a political
system or a biological organism.

I am not discounting the usefulness of the genetic model as a learning
tool but I am disputing that it be regarded as the "God of Evolution".


Hans Andersen
University of Queensland
Australia
From: genetic+@pitt.edu (Dr. Dave)
Newsgroups: comp.ai.genetic
Subject: Re: GA versus Simulated Annealing
Date: 22 Jan 93 21:57:06 GMT
Organization: Department of Industrial Engineering

Thus spake gblais@McRCIM.McGill.EDU (Gerard Blais):

>I have been using a genetic algorithm to solve a minimization
>problem.  I performed the same minimization using the "Very Fast
>Simulated Reannealing" algorithm created by Lester Ingber and
>Bruce Rosen (I got it from a friend, but I believe it's available
>from the net).  In all cases I tried, the Annealing search was
>orders of magnitude faster for converging to the global minimum
>than the GA.  

>My question is:  Are there any cases that you know of where a
>genetic algorithm would actually perform better than simulated
>annealing?  Are there any cases where simulated annealing would
>fail but not genetic algorithms?

>Any comments?


I've been waiting for someone from a more "traditional" GA background to
address this, but I haven't really seen anything yet.  I'll jump in with
both feet.

What kind of minimization problem are you doing?  Is it continuous, discrete,
or mixed?  Constrained, or unconstrained?  Unimodular, unimodular with noise,
or extremely lumpy?  

As far as I can tell, the main (potential) advantage that GA offers over SA
and its kin is in the area of robustness.  For easy problems, SA is faster
and just as good.  For hard problems, it's not so clear.  For extremely hard
problems on tricky search spaces with weird constraints, GA *should* do much
better.

I say "should" above because, as far as I know, nobody has done a systematic
study on this.  However, it seems to be not uncommon, at least when looking at
combinatorial problems, to see cases where SA is faster, and running a lot of
different SA runs will find you solutions just as good as the best GA runs, 
but the *average* value of the GA solutions is better than the average value
of the SA solutions, and the quality of the GA is less sensitive to parameter
settings (pop size, mutation rate, encoding and operators, etc.) than the SA
is to its parameters (cooling schedule, settle time at a given temp., etc.).

I'd be happy to be corrected about any of the above, if anyone out there has
different experience/intuition...

Dave Tate

-- 
       David Tate        |"Something there is that doesn't love a wall,
 dtate@unix.cis.pitt.edu | That sends the screaming liner over it 
 Prof. of Story Problems | And spills the bleacher-dwellers from their seats."
 Member ORSA, TIMS, SABR |  	"Fenway Wall", Robert Frost          
Newsgroups: comp.ai.genetic
From: jek@Xenon.Stanford.EDU (James E. Kittock)
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Organization: Computer Science Department, Stanford University.
Date: Thu, 21 Jan 1993 09:29:27 GMT

In article <1jlmb2INNjp4@gaia.ucs.orst.edu> choup@ava.bcc.orst.edu (Ping Chou) writes:

>        Or the development of genetic algorithms has
>completed?

I doubt anyone would argue this.  For example, genetic
programming just came on the scene within the past
couple of years and it has the potential to churn up
the field significantly.

>        Don't you think you may be able to discover new algorithms from
>studying genetics?

Of course.  But geneticists calling us stupid and
telling us to go back to school doesn't help, does it?
In particular, I happen to be intrigued by the fact
that genes not only produce physical structure and
regulate cellular events, they effectively regulate
their own expression.  This sort of feedback should be
very interesting to investigate!

>        Do you really believe the complete genome of HIV is just a 4-state
>bits encoded string?

Well, in a sense, sure.  Why?  What do you know that we
don't?  How about educate us rather than chastise us!

>        Then please, give youself a chance, take a look at any modern
>genetics text book, and be a cross-discipline expert.

It is pretty arrogant to assume that people who work
with genetic algorithms have no knowledge of genetics!
Furthermore, different people have different interests,
and you can't be sure what each person ought to study.
For example, there is relatively little need for
someone interested in modelling the evolution of
populations to have a thorough understanding of, say,
molecular biology even though it relates to genetics.

>        BTW, I would appreciate any reasonable arguments because I'm trying
>to learn ai-genetic from the genetics side.

Well, I suggest you go back to school and take a course
in each of the following: programming, data structures,
algorithms, parallel algorithms, automata theory, etc.
etc. etc.  It's a joke, get it?

For those non-biologists interested in getting some
background on cell physiology and/or genetics, I highly
recommend "The Living Cell", by Christian de Duve from
the Scientific American library series.  It is getting
a bit "old" (1985?) but I assume the basic top-level
picture it presents is still mostly "correct".  More
importantly it is scientific without being pedantic and
boring and yet it is not too "popular".

And I would have to second Ping's suggestion to study
cross-disciplinarily.  It can be surprisingly useful.


--james, "Don't know much about biology 'cept what I
need to"

-- 
 james kittock :: stanford cs grad student :: duke '92 :: go blue devils!!
 "Adaptation is a recursive process in living systems." -- Alex. M. Andrew
From: danwell@IASTATE.EDU (Daniel A Ashlock)
Newsgroups: comp.ai.genetic
Subject: Re: Distributed Computing of GAs
Date: 24 Jan 93 16:28:28 GMT
Reply-To: danwell@IASTATE.EDU (Daniel A Ashlock)
Organization: Iowa State University

In article <C1BxwL.5A0@comp.vuw.ac.nz>, vidya@tahi.isor.vuw.ac.nz (Srividya
Venkataraman) writes:
> 
> Hi,
>
>   I'm about to start my Masters work on Distribted Computing of Genetic
>Algorithms.  I'm new to GAs and would appreciate any information of work- past
>and present, in this area. Any ideas/suggestions/references will be greatly
>appreciated.
 

   Muhlenbein, Heinz, "Darwin's Continent Cycle Theory and Its Simulation
   bt the Prisoner's Dilemma"  Complex Systems 5 (1991) 459-478

   Muhlenbein H., Schomisch M., Born J. "The parallel genetic algorithm
   as a function optimizer" Parallel Computing (issue unknown)

   Muhlenbein H., M. Gorges-Schleuter, O. Kramer  "Evolution algorithms in
   combinatorial optimization."  Parallel Computing 7 (1988) 65-85

   Tanese, Reiko "Distributed Genetic Aglorithms" Proceedings of the 3rd
   Internation Conference on Genetic Algorithms.

   The stuff by Muhlenbein is annoying in that he incompletely specifies his
models but it is rich in references.
From: ellis@csd4.csd.uwm.edu (Thomas J Ellis)
Newsgroups: comp.ai.genetic
Subject: GA to play Tic Tac Toe
Date: 22 Jan 1993 19:07:15 GMT
Organization: University of Wisconsin - Milwaukee
Reply-To: ellis@csd4.csd.uwm.edu
NNTP-Posting-Host: 129.89.7.4
Originator: ellis@csd4.csd.uwm.edu

I have written a GA program to 'teach' the machine how to play Tic Tac Toe.
I don't have any background in GA and have run into a problem. Learning
almost doesn't take place. It has gotten a little better than a totally
random player, but a 7 year old would crush it. I am looking for references
or suggestions. I have a strong background in math and programming. Respond
to: ellis@csd4.csd.uwm.edu or
    ellis@archimedes.math.uwm.edu

It has occured to me that the GA may not be well siuted to the games tree
structure.

Tom