Introduction & overview of “artificial life”—evolving intelligent agents for modeling & simulation

ABSTRACT

"Artificial Life," despite its biological analogy and the hyperbole that its name implies, is really a collection of methods for building discrete event simulations with evolving multiple agents. It consists mainly of representing parts of systems or natural phenomena as individual active objects that may be both persistent and self-modifiable, operating on them with genetic algorithms or other evolutionary computing techniques and treating their multi-dimensional parameter (state) space discretely, by using cellular automata or similar coupled map lattices. The attractiveness of these methods for general purpose modeling and simulation lies in their ability to produce complex emergent phenomena out of a small set of relatively simple rules, constraints and relationships couched in either quantitative or qualitative terms. This tutorial includes brief introductions to both GA and CA, a description of a tool kit for building multi-agent simulations, and an outline of a current application to electric power systems and to the evolution of the electric power industry itself References are included to demonstration software and source code available on the internet.

REFERENCES

• 1: Conway, J.H. 1982. What is Life? In Winning Ways for Your Mathematical Plays. eds. E. Berlekamp, J.H. Conway and R. Guy, Vol. 2, Chap. 25. New York, NY: Academic Press
• 2: Electric Power Research Institute. 1996. Integrated Knowledge Framework (IKF) for Coal-Fired Power Plants. EPRI Technical Report TR-106211- V1/2/3, (Mar). Pleasant Hill, CA: EPRI Dist. Ctr.
• 3: Fogel, L.J., A.J. Owens, and M.J. Walsh. 1966. Artificial Intelligence Through Simulated Evolution. New York: Wiley
• 4: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 5: John R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA, 1992
• 6: Koza, J.R. 1994. Genetic Programming 1I: Scalable Automatic Programming by Means of Automatically Defined Functions. Cambridge. MA: MIT Press
• 7: Margolus, N. 1995. Ultimate Computers. In Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing (San Francisco, Feb. 15-17) eds. D.H. Bailey et al. 181- 186. Society for Industrial and Applied Mathematics, Philadelphia, PA.
• 8: Mitchell, M. and S. Forrest. 1993. Genetic Algorithms and Artificial Life. Technical Report SF193-11-072, Santa Fe Institute, Santa Fe, NM.
• 9: Nagel, K. and S. Rasmussen. 1994. Traffic at the Edge of Chaos. Technical Report SFI 94-06-032. Santa Fe Institute, Santa Fe, NM.
• 10: Rechenberg, i. 1973. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Frommann- Holzboog
• 11: Craig W. Reynolds, Flocks, herds and schools: A distributed behavioral model, ACM SIGGRAPH Computer Graphics, v.21 n.4, p.25-34, July 1987
• 12: Rucker, R. ([e-mail]). 1996. "CAPOW3 B?.ZIP," from WEB site: http://www.mathcs.sjsu.edu/capow/.
• 13: Santa Fe Institute ([e-mail]).1996. ftp://flp.santafe.edu/pub/swartn/, or from WEB site: http://www, santafe, edu/proj ec ts/swartrd.
• 14: Hans-Paul Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, Inc., New York, NY, 1981
• 15: Toffoli, T. 1995. Fine-Grained Models and Massively- Parallel Architectures: The Case for Programmable Matter. In Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing (San Francisco, Feb. 15-17) eds. D.H. Bailey et al., 181-186. Philadelphia, PA: Society for Industrial and Applied Mathematics.
• 16: Von Neumann, J. 1949. Theory of Self-Reproducing Automata. In 1949 University of Illinois Lectures on the Theory and Organization of Complicated Automata. (reprinted 1966) ed. A.W. Burks. Urbana, IL: University of lUinois Press
• 17: Wildberger, A.M. 1994. Automated Management for Future Power Networks: A Long-Term Vision. Public Utilities Fortnightly 132, 20 (Nov): 38-41.
• 18: Wuensche, A. 1996. http://www.santafe.edu/~wuensche
• 19: Wuensche, A. and M.J. Lesser. 1992. The Global Dynamics of Cellular Automata: an Atlas of Basin of Attraction Fields of One-Dimensional Cellular Automata. (diskette included). Santa Fe Institute Studies in the Sciences of Complexity. Reference Vol. I. Reading, MA: Addison-Wesley