Barbara,
First, biological coevolution benefits the genes - not the species (...it
also tends to generate environmental complexity...)... conceivably, you
could have memetic coevolution... but...
...that assumes that the timescales of the coevolving species are
comparable... there's a big mismatch between rates of human memetic
evolution (...cultural evolution...) and extrapolated machine memetic
evolution (...uh, use commercial software evolution as a baseline...).
That's why it's a singularity.
I'm afraid that if we're not going to be content with being well-behaved
ancestors... well, we're in a pickle!
Cymm
-----Original Message-----
From: Barbara Lamar <shabrika@juno.com>
To: extropians@extropy.org <extropians@extropy.org>
Date: Friday, September 29, 2000 4:42 PM
Subject: Re: Symbiotic Evolution (was Why would AI want to be friendly?)
>For anyone else interested in models of coevolution. It appears to me
>that it's necessary to build this into an AI in order to give it the
>feedback it needs from its environment--
>
>
> I found the following to be good as background material for
>understanding more recent research.
>State of knowledge in 1993:
>
>Genetic Algorithms and Artificial Life
>
>Abstract: Genetic algorithms are computational models of evolution that
>play a central role in many artificial-life models. We review the history
>and current scope of research on genetic algorithms in artificial life,
>using illustrative examples in which the genetic algorithm is used to
>study how learning and evolution interact, and to model ecosystems,
>immune system, cognitive systems, and social systems. We also outline a
>number of open questions and future directions for genetic algorithms in
>artificial-life research.
>
>http://citeseer.nj.nec.com/mitchell93genetic.html
>
>
>Cooperative Coevolution: An Architecture for Evolving Coadapted
>Subcomponents (2000)
>
>Abstract: To successfully apply evolutionary algorithms to the solution
>of increasingly complex problems, we must develop effective techniques
>for evolving solutions in the form of interacting coadapted
>subcomponents. One of the major difficulties is finding computational
>extensions to our current evolutionary paradigms that will enable such
>subcomponents to "emerge" rather than being hand designed. In this paper,
>we describe an architecture for evolving such subcomponents as a
>collection of cooperating species. Given a simple stringmatching task, we
>show that evolutionary pressure to increase the overall fitness of the
>ecosystem can provide the needed stimulus for the emergence of an
>appropriate number of interdependent subcomponents that cover multiple
>niches, evolve to an appropriate
>
>http://citeseer.nj.nec.com/potter00cooperative.html
>
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