HDTenEightyP/NetNews huggingface dataset:
From: revell@rodan.UU.NET (James R Revell Jr)
Newsgroups: comp.ai.genetic
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Date: 8 Jan 1993 20:54:17 -0500
Organization: UUNET Technologies Inc, Falls Church, VA
Reply-To: revell@uunet.uu.net (James R Revell Jr)
NNTP-Posting-Host: rodan.uu.net

In article <24659@hacgate.SCG.HAC.COM> tackett@ipld01.hac.com (Walter Alden Tackett) writes:
} In article <C0ICnz.o6@world.std.com> lains@world.std.com (Layne L Ainsworth) writes:
} 
} Larry Yaegar (sorry if I butchered the last name!) at apple (larryy@apple.com
} is his address if memory serves) has a wonderful simulation that runs on
} SGI machines.  It falls most squarely under the category of Artificial Life,
} since the evolving creatures are implicitly rewarded for survival and 
} reproduction (i.e., they must feed, defend themselves, and mate while others
} around them do the same) rather than being rewarded for an explicit externally
} imposed fitness function.  Different genotypes evolve, each with their own
} niche.  Most go extinct fairly rapidly, some flourish.  Very visual, this work
} will probably be published in the forthcoming Artificial Life III procedings
} and the software can be obtained from Larry Yaegar (I think).

I was part of a group that did a project very much like at GWU a while back,
though ours was quite non-visual.

"Survival of the Fittest".  G. Bailey, R. Demeo, H. Holz, and J. Revell.
Proceedings of the Workshop on Evolution and Chaos in Cognitive Processing
(IJCAI-91), August '91.

There is additional work, but it's not particularly related to the original
request.
-- 
James Revell	Network Services Mgr   <revell@uunet.uu.net>   /8^{~
From: tackett@ipld00.hac.com (Walter Alden Tackett)
Newsgroups: comp.ai.genetic
Subject: Re: LISP
Date: 12 Jan 93 17:56:36 GMT
Organization: Hughes Aircraft Company - Canoga Park, CA

In article <1993Jan12.153838.7079@ncsu.edu> naoursla@eos.ncsu.edu (NORMAN ALAN OURSLAND) writes:
>
>Hi. Does anyone know of a public domain LISP interpreter for UNIX, X-windows,
>or MS-DOS to use for playing around with GA? Thank you.
>
>N. Alan Oursland

Yes- akcl, Austin Kyoto Common LISP is available for Unix machines.  I use it
to run Genetic Programming.  Docummentation and debugger are everything you
would expect for free :-( , but compatibility to Steele/Common LISP standard
is excellent, and language is extensible in C.  It is about equally as fast
as Allegro CL and Lucid, *but* if you are running GP, there is no FAST-EVAL
written for it and I am far from being enuf of a LISP guru to write one.  If
anyone out there in GP land has one, or the ambition to write one, PLEASE LET
ME KNOW!

From the akcl README file:

OBTAINING SOURCES:
-----------------
* There are source files on rascal.ics.utexas.edu:pub/akcl-XX.tar.Z
You probably want the highest XX version number.  For example
akcl-1-605.tar.Z would allow you to build the version 1-605 of AKCL.  In
the following this compressed tar file is simply referred to as
akcl.tar.Z.   You will also need to obtain the original kcl distribution
of June 1987.  That is referred to as kcl.tar.Z and is also available
on rascal.   An alternate source for these files is cli.com:akcl/akcl-XX.tar.Z
In general the bandwidth to rascal is higher than to cli.com.
Rascal's address is rascal.ics.utexas.edu (128.83.138.20).

* If you cannot obtain these files via internet, a cartridge tape (sun
compatible) or diskettes containing akcl, and kcl sources may be
obtained for $250 US plus shipping, from J. Schelter, 1715
Barnswallow, Autin TX 78746.  This would be in standard tar format.
Some machines on which akcl compiles are 386 under System V (eg
Microport), Sun's (sparc,sun3's), HP under hpux and 4.3.  An older
version of sources is also available from Austin Code Works, 11100
Leafwood Ln., Austin TX 78750.  Tel:(512) 258-0785.

Good luck!

-walter

-- 
"<<my advisor>> is as nice as a guy can be and still get tenure."- Anon.
Newsgroups: comp.ai.genetic
From: HEAGYWS@ctrvax.Vanderbilt.Edu ()
Subject: Dr. Dobbs
Organization: Vanderbilt University
Distribution: usa
Date: Sun, 10 Jan 1993 03:03:23 GMT
X-News-Reader: VMS NEWS 1.24

For anyone interested, the february issue of Dr. Dobbs Journal has
COGNITIVE COMPUTING as the cover story and one of the articles is
on genetic algorithms.  The article includes source code, written 
in Turbo Prolog, of an example genetic algorithm solution to a 
problem.

Win
heagyws@ctrvax.vanderbilt.edu
=-------------------------------------=
From: rice@sumex-aim.Stanford.EDU (James Rice)
Newsgroups: comp.ai.genetic
Subject: Re: Simple problems to solve genetically
Date: 10 Jan 93 15:58:21
Organization: Knowledge Systems Lab, Stanford University
	<TOMF.93Jan10093713@ai.gte.com> <1993Jan10.213859.24981@seas.gwu.edu>
NNTP-Posting-Host: hpp.stanford.edu
In-reply-to: freytag@seas.gwu.edu's message of Sun, 10 Jan 1993 21:38:59 GMT


At the risk of sounding boring again, it seems
worthwhile pointing out that the lack of need to
come up with a magical encoded representation is
one of the key features of GP.

Although I haven't put any cycles into thinking
about blackjack (indeed games in general have
the problem that you either need a minimax
player to play against and possibly an enormous
number of fitness cases or you need to co-evolve
players - either way it's computationally
expensive), there are a bunch of problems like
this that are just screaming out to be addressed
by a method that finds a program to solve the
problem, not a mysteriously encoded character
string.



Rice.
Newsgroups: comp.ai.genetic
From: freytag@seas.gwu.edu (Richard Freytag)
Subject: Re: Simple problems to solve genetically
Followup-To: freytag@seas.gwu.edu
Organization: George Washington University
Date: Sun, 10 Jan 1993 21:38:59 GMT

Tom Fawcett writes....

I'm surprised by this discussion of how to represent blackjack so a
genetic algorithm could solve it.  If you have to put that much effort into
getting the representation just right, it seems like you -- the humans -- have
already done the interesting learning.

Richard Freytag adds....

I agree completely.  It seems that much of the effort involved in GAs is 
in how to map the problem representation onto the GA bit string so that
the search under the selected recombination operator(s) has meaning 
beyond a random search.  The generally, emphasize generally, complete
networks used in neural nets avoid this problem with the representation
topology at the cost of vastly expanded search space from any given state
vector.  I regularly see efforts that do not take significant advantage
of the linkage pattern imposed by the recombination operator with the 
string bit pattern [Goldberg, GAs & Simulated Annealing].  
The result is nearly random search that suceeds 
because there are many satisficing state vectors in the problem space and
no clear idea of what the optimum is.  Best examples can be seen in many
efforts that simulate artificial life.  These papers need to show that
consideration has been given to what the linked alleles would be and map
them as close as possible onto the bit string.  I saw an article in
AI Expert 6 monthes ago that did just that with the TSP.  Results were
presented at GA Conference IV.  Reference escapes me at the moment but
email me and I will send it too you.  

-Richard Freytag
Newsgroups: comp.ai.genetic
From: kv07@IASTATE.EDU (Warren Vonroeschlaub)
Subject: Re: Simple problems to solve genetically
Reply-To: kv07@IASTATE.EDU (Warren Vonroeschlaub)
Organization: Ministry of Silly Walks
Date: Mon, 11 Jan 1993 20:52:24 GMT

In article <1993Jan8.202702.21180@fcom.cc.utah.edu>,
freier@mail.physics.utah.edu (rodney james freier) writes:
>   I'm looking for a few simple games/problems that can be played/solved
> genetically.  I'm just trying to practice with the concept.

  Just about any game with an evaluation phase can be done (chess, checkers,
tic-tac-toe).  Just have an alpha-beta search with node rating, and evolve
different ways to rate nodes.  I did this way back with connect-four.

  I think some of the top chess programs already do this.

>  The best
> simple project I've been able to come up with so far is generating a
> Blackjack strategy genetically.

  I tried this when I first thought of genetic algorithm games.  The problem is
that it has already been thouroughly mapped out, so at best you could only match
what is already known :-(

 |  __L__
-|-  ___  Warren Kurt vonRoeschlaub
 |  | o | kv07@iastate.edu
 |/ `---' Iowa State University
/|   ___  Math Department
 |  |___| 400 Carver Hall
 |  |___| Ames, IA  50011
 J  _____
Xref: sparky comp.ai.genetic:40 comp.ai.neural-nets:4805
From: avigdor@basashi.aisoft.co.jp (Pollak Avigdor)
Newsgroups: comp.ai.genetic,comp.ai.neural-nets
Subject: GA simulator wanted. (In english)
Date: 12 Jan 93 03:23:59 GMT
Followup-To: comp.ai.genetic
Organization: A.I.SOFT,INC.  Matsumoto City. Japan

Hi !
I  am looking for available ( or cheep ) GA simulator 
that works under X-WINDOWS .
Any information about this kind of product would
be greatly appreciated.

	Thanks
From: tackett@ipld01.hac.com (Walter Alden Tackett)
Newsgroups: comp.ai.genetic
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Date: 8 Jan 93 07:31:57 GMT
Organization: Hughes Aircraft Company - Canoga Park, CA

In article <C0ICnz.o6@world.std.com> lains@world.std.com (Layne L Ainsworth) writes:
>Has anyone tried to do this: 
>
>"Herds" of cheap (really cheap), reprogrammable robots, each loaded
>with one set of the test "chromosome" population of a Genetic
>Algorithm system. They could be evaluated to optimize indivdual
>performance, or "hive" well-being.  Each "generation" would be the
>same robots, with a new generation of "chromosomes" on board.

Larry Yaegar (sorry if I butchered the last name!) at apple (larryy@apple.com
is his address if memory serves) has a wonderful simulation that runs on
SGI machines.  It falls most squarely under the category of Artificial Life,
since the evolving creatures are implicitly rewarded for survival and 
reproduction (i.e., they must feed, defend themselves, and mate while others
around them do the same) rather than being rewarded for an explicit externally
imposed fitness function.  Different genotypes evolve, each with their own
niche.  Most go extinct fairly rapidly, some flourish.  Very visual, this work
will probably be published in the forthcoming Artificial Life III procedings
and the software can be obtained from Larry Yaegar (I think).

On a less visual (but more supervised) note John Koza and James Rice have
done work with emergent behavior of ants (see ALife-2 proceedings & video, and
 JK's new book "Genetic Programming," and the accompanying video by Koza & 
Rice.).  Also see work by Collins & Jefferson (also in ALife3) and Rob Collins
dissertation, available via ftp from UCLA/cognet.

-walter alden tackett

Disclaimer: "My employers are responsible for the contents of this letter 
             because I secretly control their thoughts."



Newsgroups: comp.ai.genetic
From: arkin@cc.gatech.edu (Ronald Arkin)
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Reply-To: arkin@cc.gatech.edu (Ronald Arkin)
Organization: College of Computing, Georgia Institute of Technology
Date: Fri, 8 Jan 1993 11:42:29 GMT

In article <C0ICnz.o6@world.std.com> lains@world.std.com (Layne L Ainsworth) writes:
>
>
>Has anyone tried to do this: 
>
>"Herds" of cheap (really cheap), reprogrammable robots, each loaded
>with one set of the test "chromosome" population of a Genetic
>Algorithm system. They could be evaluated to optimize indivdual
>performance, or "hive" well-being.  Each "generation" would be the
>same robots, with a new generation of "chromosomes" on board.
>
>Pointers to papers or discussion of the feasibility of such a project
>would both be welcome. I would think this could be a job for the
>much-heralded and -scorned MiniBoard. Wire up all the connections to
>various sensors/actuators and let the herd discover uses for them.
>
>My dream, of course, is a bunch of robots that (who?) could
>continuously clean my house. :-)
>
>Thanks.
>
>-Layne
>
>
>
>-- 
>  Layne Ainsworth | lains@world.std.com <or> ...!{xylogics,uunet}!world!lains
>	  The truth can't hurt you; it's just like the dark.
>



Check out

        "The Learning of Reactive Control Parameters through Genetic
         Algortihms", M. Pearce, R. Arkin, and A. Ram, Proc. 1992
         IEEE/RSJ International Conference on Intelligent Robots and
         Systems, (IROS 92), Raleigh, NC July 1992, pp. 130-137.

These are the simulation results, we hope to port these to our Denning
Robots shortly.

- Ron
   
Ronald C. Arkin
College of Computing, Georgia Institute of Technology
Atlanta, Georgia 30332-0280
Phone: (404)-894-8209            Fax: (404)-853-9378
From: rice@sumex-aim.Stanford.EDU (James Rice)
Newsgroups: comp.ai.genetic
Subject: Re: Simple problems to solve genetically
Date: 8 Jan 93 19:42:33
Organization: Knowledge Systems Lab, Stanford University
NNTP-Posting-Host: hpp.stanford.edu
In-reply-to: freier@mail.physics.utah.edu's message of Fri, 8 Jan 93 20:27:02 GMT

There are a couple of examples of the evolution of
game playing strategies in Koza's book (and movie),
but nothing too complex.

Angeline at Ohio state has tinkered with tic-tac-toe using
GP.



Rice.
From: time@ice.com (Tim Endres)
Newsgroups: comp.ai.genetic
Subject: Re: Anyone know of a good intro paper/book?
Date: Tue, 12 Jan 93 22:02:15 EST
Organization: ICE Engineering, Inc.
Reply-To: time@ice.com (Tim Endres)
X-Mailer: uAccess - Macintosh Release: 1.6v2


In article <RICE.93Jan12085252@hpp-ipc-2.stanford.edu> (comp.ai.genetic), rice@sumex-aim.Stanford.EDU (James Rice) writes:
> I haven't read Holand's book, but I gather that it is absolutely
> NOT the right place to start.

This is very correct.


tim endres - time@ice.com  (UUCP: heifetz!tbomb!time)

USENET - a slow moving self parody... ph
From: jet@boxer.nas.nasa.gov (J. Eric Townsend)
Newsgroups: comp.ai.genetic
Subject: Anyone know of a good intro paper/book?
Date: 11 Jan 93 22:40:17 GMT
Organization: Numerical Aerodynamic Simulation, NASA Ames


Anybody have a suggestion for a good introductory paper or book?  I've
seen a few interesting papers, but they assume one is already *very*
familiar with the thy and terminology involved.

thx.

--
J. Eric Townsend --  jet@nas.nasa.gov -- 415.604.4311 (DoD# 0378)
"It is the cunning of form to veil itself continually in the evidence
of content.  It is the cunning of the code to veil itself and to produce
itself in the obviousness of value." -- Baudrillard
Newsgroups: comp.ai.genetic
From: lynwood@teal.csn.org (Lynwood H. Wilson)
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Organization: Colorado SuperNet, Inc.
X-Newsreader: Tin 1.1 PL4
Date: Tue, 12 Jan 1993 23:24:58 GMT

Is this paper available via ftp?
Lynwood
Newsgroups: comp.ai.genetic
From: freier@mail.physics.utah.edu (rodney james freier)
Subject: Re: Simple problems to solve genetically
Organization: University of Utah - Physics Department
Date: Sat, 9 Jan 93 19:26:44 GMT

In article <LAINS.93Jan8190731@world.std.com>, lains@world.std.com (Layne L Ainsworth) writes:
|> 
|> On the BlackJack problem:
|> 
|> aIt might be knd of interesting to give as input the history of all
|> cards turned up in the game (current shoe). Emergent card counting!

   Yes, that should give a better playing strategy, but at a huge cost.
To make a hit/stay decision, you need to know the value of your hand and
some reasonable number of the cards that have appeared up to this point.  
This should always include the dealer's upcard and your two cards.  (Note that
I am not assuming any other players in the game.  I am also not allowing the
computer enough info to allow for splitting or doubling down, two things
you really need to use well to cut the bank's advantage.  I am ALSO assuming
that ONE deck is reshuffled after every play.  A three-deck shoe would make
these numbers worse.)  
This doesn't seem much more complicated than the original problem statement, 
but let's see:

   Item             Number of possiblilities
   Hand's value       9  (assume always stay on 19 and always hit on 10)
   3 cards seen        22,100       All of these numbers assume that
   4 cards seen        270,725       you have some nice way of storing the cards
   5 cards seen        2,598,960      compactly and non-redundantly.

  The algorithm has to be able to make a decision under any case:  3 cards known,
4 or 5.   If six cards are known, you might want to just assume that only 5 are,
and go with that.  Now....  each string must give a 1 or a 0 (hit/stay) for
each possibility.  That gives a total string length of
                                                             .
       9*(22,100 + 270,725 +  2,598,960) =  26,026,065 bits = 3.2 megs.

  You do NOT want to use strings this large.  you would need gigabytes
of quickly accessible memory during a run if you expect to sample a significant 
region of the problem space.

    Let me compare that with the string size for the original problem.

     Item             Number of possibilities
     Hand's value        9
     dealers upcard      52
     Is hand splitable?  2
      
    This gives only 9*2*52 = 936 bits per string, or 117 bytes.  That would
mean you could deal with a significant population size in about 500k total.
Even PC's can deal with this.

Rod

Newsgroups: comp.ai.genetic
From: HEAGYWS@ctrvax.Vanderbilt.Edu ()
Subject: Re: Dr. Dobbs
Organization: Vanderbilt University
Distribution: usa
Date: Sun, 10 Jan 1993 16:46:14 GMT
X-News-Reader: VMS NEWS 1.24

> on genetic algorithms.  The article includes source code, written 
> in Turbo Prolog, of an example genetic algorithm solution to a 
> problem.
>

Sorry...the article says the code is written in Turbo Prolog, but it looks
much more like Pascal to me.  It would have been nice to see it in 
Prolog though.

Win
heagyws@ctrvax.vanderbilt.edu
=-----------------------------------------------=
From: fzgoldth@othello.ucdavis.edu (Ron Goldthwaite)
Newsgroups: comp.ai.genetic
Subject: Re: Anyone know of a good intro paper/book?
Date: 12 Jan 93 19:17:34 GMT
Organization: Computing Services, UC Davis

Two tutorial books are available, both quite good:

Goldberg, David E.  GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION AND MACHINE
LEARNING, 1989, Addison-Wesley (a second edition is due 'soon').  Gives a 
thorough grounding in both GA for function optimization and for learning
systems ('classifier systems').  Code developed in Pascal.  Theory is 
given equal time, and presented lucidly.

Koza, John.  GENETIC PROGRAMMING: ON THE PROGRAMMING OF COMPUTERS BY
   MEANS OF NATURAL SELECTION, 1992, MIT Press, 840 pages.  270 Illustrations.  ISBN 0-262-11170-5.  A videotape is available to accompany this.  Koza
reviews GA results relevent to his theme of GP, in a fresh, intuitively
appealing discussion, including current literature.  The bulk of the
book is about 20 fully described examples of the use of GP to solve
problems from many disciplines: lizard foraging to logic circuits.  
LISP code is included as an appendix for reader experiments.

A book with a primarily pragmatic intent is 
Davis, Lawrence (Ed.), HANDBOOK OF GENETIC ALGORITHMS, 1991, Van Nostrand.

There's an excellent moderated digest, GADISTR, available by 
subscription request to GA-List-Request@AIC.NRL.NAVY.MIL


Since you're apparently at NASA-Ames, I'd recommend a visit to the
Stanford Bookstore - the downtown branch, and perhaps the campus 
branch, have these in stock usually.  

Enjoy - Ron


R. Goldthwaite, Psychology and Animal behavior     Career trajectory: from
Dept. Psychology, U.California			Artifical Intelligence (AI),
Davis, CA 95616           (916) 752 5655/1880	to Natural Stupidity (NS).
rogoldthwaite@ucdavis.edu

-- 
R. Goldthwaite, Psychology and Animal behavior     Career trajectory: from
Dept. Psychology, U.California			Artifical Intelligence (AI),
Davis, CA 95616           (916) 752 5655/1880	to Natural Stupidity (NS).
rogoldthwaite@ucdavis.edu
Newsgroups: comp.ai.genetic
From: danwell@IASTATE.EDU (Daniel A Ashlock)
Subject: Non-blackjack simple problem.
Reply-To: danwell@IASTATE.EDU (Daniel A Ashlock)
Organization: Iowa State University
Date: Mon, 11 Jan 1993 18:17:33 GMT

   In Goldbergs book (on Page 108) is a set of five "standard"
functions (over the reals) to optimize.  These get around the
difficulty "How do I code blackjack?!?" by not being blackjack.
They're good for learning how to do GA's in an environment where
they are well understood and you know what will happen.  Goldberg
himself (in his book) will tell you what ought to happen :).

Dan
Danwell@IASTATE.EDU
From: huhr@gold.uni-paderborn.de (Holger Uhr)
Newsgroups: comp.ai.genetic
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Date: 12 Jan 1993 16:08:52 +0100
Organization: Universitaet Paderborn, Germany
NNTP-Posting-Host: gold.uni-paderborn.de
X-Newsreader: TIN [version 1.1 PL7]

Robert Mah (rmah@panix.com) wrote:
: In <24659@hacgate.SCG.HAC.COM> tackett@ipld01.hac.com (Walter Alden Tackett) writes:
: 
: >Larry Yaegar (sorry if I butchered the last name!) at apple (larryy@apple.com
: >is his address if memory serves) has a wonderful simulation that runs on
: >SGI machines.  It falls most squarely under the category of Artificial Life,
: >since the evolving creatures are implicitly rewarded for survival and 
: >reproduction (i.e., they must feed, defend themselves, and mate while others
: >around them do the same) rather than being rewarded for an explicit externally
: >imposed fitness function.  Different genotypes evolve, each with their own
: >niche.  Most go extinct fairly rapidly, some flourish.  Very visual, this work
: >will probably be published in the forthcoming Artificial Life III procedings
: >and the software can be obtained from Larry Yaegar (I think).
: 
: I think it's also available via anonymous ftp at "ftp.apple.com" and maybe
: at "ftp.cognet.ucla.edu" as well.
: 
: Cheers,
: Rob
: 

Could someone post the whole path to the file(s), please?


Thank you,

Holger Uhr

-- 
|   Holger Uhr  ***   Engernweg 32  ***  W-4790 Paderborn   ***   Germany   |
|E-Mail: huhr@uni-paderborn.de                            Phone: 05251-58950|
|How to become immortal: Read this signature tomorrow and follow its advice.|
Newsgroups: comp.ai.genetic
From: freytag@seas.gwu.edu (Richard Freytag)
Subject: Re: Use of Genetic Algorithms to evolve robot behavior?
Followup-To: freytag@seas.gwu.edu
Summary: We are doing something like this
Organization: George Washington University
Date: Fri, 8 Jan 1993 09:39:07 GMT

My Co., Global Associates, is doing work in this area for DARPA.  We are
using collective behavior in robots to survey an area.  The Behavior isn't
necessarily evolved with a GA but the ant analogy is very close.  

-Richard Freytag
From: rice@sumex-aim.Stanford.EDU (James Rice)
Newsgroups: comp.ai.genetic
Subject: Re: Source for Koza's book available via ftp?
Date: 10 Jan 93 12:14:27
Organization: Knowledge Systems Lab, Stanford University
NNTP-Posting-Host: hpp-ipc-2.stanford.edu
In-reply-to: kaveh@CAM.ORG's message of 9 Jan 93 20:50:38 GMT

In article <KAVEH.93Jan9155038@altitude.CAM.ORG> kaveh@CAM.ORG (Kaveh Kardan) writes:


   Is the source code from John Koza's book _Genetic Programming_ available
   via ftp?

   Thanks,

   Kaveh

Not yet, but I'll be mailing it to genetic-programming@cs.stanford.edu
in a couple of days.



Rice.  (subscriptions to genetic-programming-request, of course)
Newsgroups: comp.ai.genetic