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Alife Digest Number 001
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Artificial Life Digest Vol. 1 Num. 1
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Topics: Artificial Life Digest Announcement
PD comments from Dawkins
a CA query
Abstraction of Emergent Systems
Abstract for Congress of Systematic and Evolutionary Biology
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ALIFE becomes Store-and-Forward *and* Relector
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The Artificial Life List will now be offered in both store-and-forward
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Elisabeth Freeman
Eric Freeman
Marek Lugowski
Indiana University Artificial Life Research Group
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Date: 05 Mar 90 00:02:00 PST
From: UK0053@applelink.apple.com
Subject: Biomorphs
Dear Larry
I haven't anything very helpful for Mr Lugowski I'm afraid. I was aware of
Axelrod's genetic algorithm work, but was never particularly clear about what
it meant to say that the strategies that he bred were 'better' than Tit for
Tat. There is also work by Boyd & Lorberbaum showing, as I recall, that Tit
for Tat might actually be invaded, in the evolutionary sense, by a mixture of
an arch-forgiving strategy (Tit for Two Tats) and a slightly nasty strategy
(Suspicious Tit for Tat). That reference is
R.Boyd & J.P.Lorberbaum (1987) No pure strategy is evolutionarily stable in the
repeated Prisoner's Dilemma game. Nature 327, 58-9.
But that is a theoretical result; they didn't breed anything.
My own Blind Watchmaker program is not fully described in the book, but is
slightly more so in my chapter the book of the first Artificial Life
conference, ed C.Langton (1989) Artificial Life. I'm not sure what he means by
'primitives'. Certainly my program uses asexual reproduction only, and it only
evolves 'knobs that are already there'. I agree that it is nice to evolve
things as far as possible from 'scratch'. All I can say there is that I
deliberately gave little thought to the knobs themselves, just shoved down into
the program the first thing that came into my head and relied on evolution to
do all the work.
No I've never carried out my ambition to let insects do the evolving. I did
once take a colour monitor out into the garden one fine sunny day and was
driven straight back in again - the sun was so bright that I couldn't see the
image at all, and presumably the insects wouldn't have done either. I decided
that one day I'd try night-flying moths.
Best wishes
Richard
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In-Reply-To: Your message of Fri, 02 Mar 90 22:48:49 -0700.
<9003030548.AA25865@cardinal.lanl.gov>
Date: Mon, 05 Mar 90 07:54:59 PST
From: Stephen Smoliar <smoliar@vaxa.isi.edu>
Howard Gutowitz wrote:
> Be on the lookout for the proceedings of "CA '89"
> to appear in the fall in Physica D. It will
> contain some excellent surveys on computation
> theory of CA, some dealing explicitly with
> connections to physics and biology.
Writing from a laboratory which does not subscribe to PHYSICA D, I would
appreciate it if, when this issue appears, someone might take the trouble
to post the table of contents to this mailing list.
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Posted-Date: Mon, 05 Mar 90 09:32:37 -0800
Subject: Emergent vs programmable levels of abstraction
From: Russ Abbott <abbott@aerospace.aero.org>
Replying to Ken Presting who writes (in the comp.ai newsgroup):
| ...
||In the second place, the dynamical properties of a running computer are
||neatly determined by its program. The program is of course a static
||object ....
||
Stephen Smoliar writes:
| ... [I]f there were a clean
|relationship between the static properties of a program and the dynamic
|properties of the device running that program, we wouldn't have all the
|software problems we have, would we? ...
|I would argue that the reason for this is
|that we still lack good ways to describe and reason about the dynamic
|properties of processes. The best we have been able to do, thus far,
|is the abstract those processes into static objects. ... [B]ut the
|power of this approach is only as good as the abstraction
|we develop. Finding the right abstraction often remains the intractable
|problem in software engineering.
But we are often quite successful in building static objects (programs)
that exhibit the dynamic properties that we desire, e.g., language
processors/interpreters. So we do know how to produce emergent
properties in a great many cases. In those cases we call it building
levels of abstraction. On the other hand, we don't know how to program
neural nets. (I'm distinguishing "training" from programming.)
So I wonder whether there is a well characterizable difference between
programmable levels of abstraction and "emergent" levels of abstraction.
Or will we eventually be able to program any system that is capable of
exhibiting emergence once we develop the right abstractions.
One obvious difference between most explicitly programmed levels of
abstraction and most emergent systems is the degree of parallelism. Is
it likely that the abstractions we will have to develop to program
highly parallel emergent systems will resemble in their discreteness
those we now use to program traditional levels of abstractions. Or will
they necessarily be more statistical and hence always resemble
"training" or environmental molding more than programming.
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Date: Sun, 4 Mar 90 14:43:03 EST
From: RAY <iuvax!vax1.acs.udel.edu!ray>
Abstract submitted to the Fourth International Congress of Systematic and
Evolutionary Biology, to be held in College Park Maryland, July 1-7, 1990:
Artificial Life: ecology and evolution in digital organisms.
THOMAS S. RAY. University of Delaware, Newark, DE, 19716, USA,
ray@vax1.acs.udel.edu.
Artificial organisms have been created based on a computer metaphor of
organic life in which CPU time is the ``energy'' resource and memory is
the ``material'' resource. Memory is organized into informational
patterns that exploit CPU time for self-replication. Mutation generates
new forms, and evolution proceeds by natural selection as different
genotypes compete for CPU time and memory space. This evolution
in a bottle may prove to be a valuable tool for the study of
evolution and ecology.
The creatures are self-replicating computer programs, however, they can not
escape because they run exclusively on a virtual computer in its unique
assembler language. Only one creature has been designed; it is 80 instructions
long and contains only the code for self-replication. From this ancestor
there have evolved many thousands of self-replicating genotypes of many
hundreds of genome size classes. Very quickly there evolved parasites, then
there evolved creatures that were immune to parasites, and then parasites that
could circumvent the immunity.
The only kind of genetic change that the simulator imposes on the system is
random bit flips in the machine code of the creatures. However, it turns
out that the parasites are very sloppy replicators, and often pick up
pieces of code from their hosts. They cause significant recombination
and rearrangement of the genomes.
Diverse ecological communities have emerged. These digital communities
have been used to experimentally examine ecological and evolutionary
processes: e.g., competitive exclusion and coexistence, host/parasite
density dependent population regulation, the effect of parasites in
enhancing community diversity, the evolutionary arms race, punctuated
equilibrium, and the role of chance and historical factors in evolution.
It is possible to extract information on any aspect of the system
without disturbing it, from phylogeny or community structure through time
to the ``genetic makeup'' and ``metabolic processes'' of individuals.
Artificial Life demonstrates the power of the computational approach to
science as a complement to the traditional approaches of experiment and
theory based on analysis through calculus and differential equations.
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