============================================================================ Artificial Life Digest Vol. 1 Num. 1 ------------------------------------ Topics: Artificial Life Digest Announcement PD comments from Dawkins a CA query Abstraction of Emergent Systems Abstract for Congress of Systematic and Evolutionary Biology ============================================================================ ALIFE becomes Store-and-Forward *and* Relector ---------------------------------------------- The Artificial Life List will now be offered in both store-and-forward and reflector formats. By default everyone has been put on the store-and-forward list. If you would like to be changed to the refector list (or both) please send an additional mail message to alife-request. As usual: All additions/deletions/administrivia: alife-request@iuvax.cs.indiana.edu All materials for DISTRIBUTION: alife@iuvax.cs.indiana.edu Elisabeth Freeman Eric Freeman Marek Lugowski Indiana University Artificial Life Research Group ---------------------------------------------------------------------------- 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 ---------------------------------------------------------------------------- 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 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. ---------------------------------------------------------------------------- Posted-Date: Mon, 05 Mar 90 09:32:37 -0800 Subject: Emergent vs programmable levels of abstraction From: Russ Abbott 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. ---------------------------------------------------------------------------- Date: Sun, 4 Mar 90 14:43:03 EST From: 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. =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= = Artificial Life Distribution List = = = = All submissions for distribution to: alife@iuvax.cs.indiana.edu = = All list subscriber additions, deletions, or administrative details to: = = alife-request@iuvax.cs.indiana.edu = = All software, tech reports to Alife depository through = = anonymous ftp at iuvax.cs.indiana.edu in ~ftp/pub/alife = = = = List maintainers: Elisabeth Freeman, Eric Freeman, Marek Lugowski = = Artificial Life Research Group, Indiana University = =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=