Abstract
Searching for signatures of fossil or present life in our solar system requires autonomous devices capable of investigating remote locations with limited assistance from earth. Here, we use an artificial chemistry model to create spatially complex chemical environments. An autonomous experimentation technique based on evolutionary computation is then employed to explore these environments with the aim of discovering the chemical signature of small patches of biota present in the simulation space. In the highly abstracted environment considered, autonomous experimentation achieves fair to good predictions for locations with biological activity. We believe that artificially generated biospheres will be an important tool for developing the algorithms key to the search for life on Mars.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Toulmin, S., Goodfield, J.: The Architecture of Matter. Harper & Row, New York (1962)
Anonymous: Astrobiology roadmap. U.S. National Aeronautics and Space Administration (NASA), (November 2002), Available at http://astrobiology.arc.nasa.gov/roadmap/roadmap.pdf
Langton, C.G.: Artificial life. In: Langton, C.G. (ed.) Artificial Life. SFI Studies in the Science of Complexity, pp. 1–45. Addison-Wesley, Redwood City (1989)
Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial chemistries—a review. Artificial Life 7, 225–275 (2001)
Langley, P., Simon, H.A., Bradshaw, G.L., Zytkow, J.M.: Scientific discovery: Computational exploration of the creative processes. MIT Press, Cambridge (1987)
de Jong, H., Rip, A.: The computer revolution in science: steps towards the realization of computer-supported discovery environments. Artificial Intelligence 91, 225–226 (1997)
Rajamoney, S.A.: A computational approach to theory revision. In: Shrager, J., Langley, P. (eds.) Computational Models of Scientific Discovery and Theory Formation, pp. 225–253. Morgan Kaufmann, San Mateo (1990)
Karp, P.D.: Hypothesis formation as design. In: Shrager, J., Langley, P. (eds.) Computational Models of Scientific Discovery and Theory Formation, pp. 275–317. Morgan Kaufmann, San Mateo (1990)
Kulkarni, D., Simon, H.A.: Experimentation in machine discovery. In: Shrager, J., Langley, P. (eds.) Computational Models of Scientific Discovery and Theory Formation, pp. 255–273. Morgan Kaufmann, San Mateo (1990)
Gooding, D.: Experiment and the Making of Meaning. Kluwer Academic, Dordrecht (1990)
Stolorz, P., Cheeseman, P.: Onboard science data analysis: Applying data mining to science-directed autonomy. IEEE Intelligent Systems & Their Applications 13, 62–68 (1998)
Gilmore, M.S., Castano, R., Mann, T., Anderson, R.C., Mjolsness, E.D., Manduchi, R., Saunders, R.: Strategies for autonomous rovers at mars. J. Geo. Phys. Res. Planets 105, 29223–29237 (2000)
Pfaffmann, J.O., Zauner, K.P.: Scouting context-sensitive components. In: Keymeulen, D., Stoica, A., Lohn, J., Zebulum, R.S. (eds.) The Third NASA/DoD Workshop on Evolvable Hardware, pp. 14–20. IEEE Comp. Soc., Los Alamitos (2001)
Cherry, C.: On Human Communication: A Review, a Survey, and a Criticism, 2nd edn. ch. 5. MIT Press, Cambridge (1966)
Beyer, H.G.: The theory of Evolution Strategies. Springer, Berlin (2001)
Matsumaru, N., Colombano, S., Zauner, K.P.: Scouting enzyme behavior. In: Fogel, D.B., et al. (eds.) 2002 World Congress on Computational Intelligence, pp. 19–24. IEEE, Piscataway (2002)
Adamatzky, A.: Identification of Cellular Automata. Taylor and Francis, London (1994)
Speroni di Fenizio, P., Dittrich, P.: Artificial chemistry’s global dynamics. movement in the lattice of organisation. The Journal of Three Dimensional Images 16, 160–163 (2002)
Fontana, W., Buss, L.W.: ’The arrival of the fittest’: Toward a theory of biological organization. Bull. Math. Biol. 56, 1–64 (1994)
Speroni di Fenizio, P., Dittrich, P., Ziegler, J., Banzhaf, W.: Towards a theory of organizations. In: German Workshop on Artificial Life, Bayreuth, April 5-7 (2000) (in print)
Yung, Y.L., DeMore, W.B.: Photochemistry of Planetary Atmospheres. Oxford University Press, New York (1999)
Ihaka, R., Gentleman, R.: R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics 5, 299–314 (1996)
Fraley, C., Raftery, A.E.: MCLUST: Software for model-based clustering, discriminant analysis and density estimation. Technical Report 415, Department of Statistics, University of Washington, Seattle, WA (2002), Available at: http://www.stat.washington.edu/www/research/reports/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Centler, F., Dittrich, P., Ku, L., Matsumaru, N., Pfaffmann, J., Zauner, KP. (2003). Artificial Life as an Aid to Astrobiology: Testing Life Seeking Techniques. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-39432-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20057-4
Online ISBN: 978-3-540-39432-7
eBook Packages: Springer Book Archive