Polyworld
An Artificial Life System and Computational Ecology
Recent news:
A few modern, high-quality Polyworld Movies have been added to the site.
The initial setup and build process for Polyworld has been somewhat streamlined and documented with careful step-by-step instructions.
Polyworld has been updated to support parallel "driven" vs. "passive" (using McShea's terminology) simulations in order to investigate the existence and nature of any evolutionary "arrow of complexity". It also supports the use of neural complexity (after Sporns's work) as an evolutionary fitness function.
Two Polyworld-based papers were presented at the ALife X conference. Links to the papers may be found below. One paper addresses a common behavioral ecology/evolutionary biology issue—how agents distribute themselves given limited, patchy resources—while the other paper applies an information-theoretic measure of complexity to the neural functioning of agents in Polyworld, providing both a long sought quantitative assessment of complexity in such systems and evidence for a statistically significant increase in complexity over evolutionary timescales.
Polyworld
"lives" again! As a
result of the research freedom offered by my faculty position in the School of
Informatics at Indiana University, I have, with the extensive help of Gene
Ragan and Nicolas Zinovieff, revived and modernized Polyworld. It has been ported to run atop Qt, from
Trolltech, and OpenGL, so it should be fully cross-platform
(Mac OS X, Windows, and Linux) and should run on any modern personal computer
or workstation, although only Mac OS X and Linux versions have been
successfully deployed so far. The
app has about 90% of its old functionality, and the remaining 10% is mostly
just user interface stuff—the core simulation engine is fully functional, I
believe. This new version of the source code was released via SourceForge on
December 25, 2004 and may be accessed at http://sourceforge.net/projects/polyworld/. Substantial new capabilities have since
been added.
Polyworld
background and details:
Polyworld is a computational ecology that I developed to explore issues in Artificial Life. Simulated organisms reproduce sexually,
fight and kill and eat each other, eat the food that grows throughout the
world, and either develop successful strategies for survival or die. An
organism's entire behavioral suite (move, turn, attack, eat, mate, light) is
controlled by its neural network "brain". Each brain's
architecture--it's neural wiring diagram--is determined from its genetic code,
in terms of number, size, and composition of neural clusters (excitatory and
inhibitory neurons) and the types of connections between those clusters (connection
density and topological mapping). Synaptic efficacy is modulated via Hebbian
learning, so, in principle, the organisms have the ability to learn during the
course of their lifetimes. The organisms perceive their world through a sense
of vision, provided by a computer graphic rendering of the world from each
organism's point of view. The organisms' physiologies are also encoded
genetically, so both brain and body, and thus all components of behavior,
evolve over multiple generations. A variety of "species", with
varying individual and group survival strategies have emerged in various
simulations, displaying such complex ethological behaviors as
swarming/flocking, foraging, and attack avoidance.
For further information, please feel free to take a look at:
Yaeger, L. S. 1994. Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context. Langton, C. ed. Proceedings of the Artificial Life III Conference. 263-298. Addison-Wesley.
- A preprint of a paper submitted and accepted for the ALife XI conference that uses Polyworld to address the distinction between passive and driven trends in the evolution of complexity (after McShea) and I like to think partially resolves a debate between Richard Dawkins and Stephen J. Gould (they were talking at different scales; Dawkins was right at his; Gould may have been right at his, but reasonable doubt remains):
Yaeger, L. S., Griffith, V., and Sporns, O. 2008 (accepted). Passive and Driven Trends in the Evolution of Complexity. In Bullock, S. et al. eds. Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. MIT Press. Cambridge, MA.
- Two very different papers presented at the ALife X conference, one on optimal foraging, the other on evolving complexity:
Griffith, V. and Yaeger, L.S. 2006. Ideal Free Distribution in Agents with Evolved Neural Architectures, in Rocha, L. et al. eds. Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. 372-378. MIT Press. Cambridge, MA.
Yaeger, L. S. and Sporns, O. 2006. Evolution of Neural Structure and Complexity in a Computational Ecology, in Rocha, L. et al. eds. Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems. 330-336. MIT Press. Cambridge, MA.
- Slides from a 2/27/06 talk for the Networks and Complex Systems Talk Series:
Yaeger, L. S. and Sporns, O. 2006. Evolution of Neural Complexity.
The README.txt file has some info on the subject, but complete instructions for installing and building Polyworld are here. There is also a short list of outstanding to-do items in the TODO.txt file, divided into simple code restoration/maintenance tasks and basic research directions.
- Imagina and INA have a CyberScience site that has images and low resolution, fairly low quality short movies from Polyworld.
- By popular demand (okay, somebody actually asked), I
have put some higher quality Polyworld
movies on the web.
I've had an ongoing interest in the subject of Artificial Life since 1987 (and in related subjects, such as how the mind works, since even earlier), and have placed some of my notes and conference reports in the ALife section on this server.
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