Skip to main content

The Impact of Environmental Structure on the Evolutionary Trajectories of a Foraging Agent

  • Conference paper
  • First Online:
Artificial Evolution (EA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2310))

  • 599 Accesses

Abstract

A foraging agent using a sensorimotor controller is simulated in environments with varying ecological structure. The controller is evolved in the different environments to produce a range of emergent behaviours, which are analysed and compared using data reduction techniques: the behaviours are compared between environments and in their evolutionary trajectories. The relationship between the evolutionary trajectories, the affordances in the different environments, and the performance and onward evolution of controllers in their non-native environments is explored. The different environments have lead to agents following different evolutionary trajectories and arriving at similar but slightly different behaviours. These evolved controllers then evolve differently when challenged with a new environment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Edmonds, I. R., 2001, The Use of Latent Semantic Indexing to Identify Evolutionary Trajectories in Behaviour Space, in (eds) Kelemen, J., and Sosik, P., Advances in Artificial Life, 6th European Conference, ECAL 2001, LNCS; 2159, LNAI, Springer-Verlag

    Google Scholar 

  2. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R., 1990, Indexing by Latent Semantic Analysis, Journal of the American Society for Information Science, 41 (6), 391–407

    Google Scholar 

  3. Landauer, T. K., and Dumais, S. T., 1997, A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge, Psychological Review, 104 (2), 211–240

    Google Scholar 

  4. Gomez, F., and Miikkulainen, R., 1997, Incremental Evolution of Complex General Behavior, Adaptive Behavior, vol. 5, no. 3/4, 317–342

    Google Scholar 

  5. Seth, A. K., 1998, Evolving Action Selection and Selective Attention Without Actions, Attention, or Selection, in Pfeifer, R., Blumberg, B., Meyer, J-A., and Wilson, S. W., (eds), Animals to Animats 5, Proceedings of 5th International Conference on Simulation of Adaptive Behavior, Bradford Book, MIT Press

    Google Scholar 

  6. Shipman, R., 1999, Genetic Redundancy: Desireable or Problematic for Evolutionary Adaption?, The 4th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA’ 99), April 1999

    Google Scholar 

  7. Nol., S., 1997, Evolving non-trivial behaviours on real robots: Agarbage collecting robot, Robotics and Autonomous Systems, 22, 187–198

    Google Scholar 

  8. Nol., S., 1997, Using Emergent Modularity to Develop Control Systems for Mobile Robots, Adaptive Behaviour, vol. 5, no. 3/4, 343–363.

    Google Scholar 

  9. Calabretta, R., Nol., S., Parisi, D., and Wagner, G. P., 1998, Emergence of Functional Modularity in Robots, in Pfeifer, R., Blumberg, B., Meyer, J-A., and Wilson, S. W., (eds), Animals to Animats 5, Proceedings of 5th International Conference on Simulation of Adaptive Behavior, Bradford Book, MIT Press.

    Google Scholar 

  10. Foster, D. J., Morris, R. G. M., and Dayan, P., 2000, Models of Hippocampally Dependent Navigation, Using The Temporal Difference Learning Rule, Hippocampus, vol. 10, issue 1

    Google Scholar 

  11. Husbands, P., Harvey, I., and Cli., D., 1995, Circle in the round: State space attractors for evolved sighted robots, Robotics and Autonomous Systems, 15, 83–106

    Google Scholar 

  12. Thelen, E., 1995, Motor Development, American Psychologist, Feb 95, 79–95

    Google Scholar 

  13. Beer, R. D., 2000, Dynamical approaches to cognitive science, Trends in Cognitive Sciences, vol 4, no 3, 91–99

    Google Scholar 

  14. Moriarty, D. E., and Miikkulainen, R., 1998, Forming Neural Networks Through Efficient and Adapted Coevolution, Evolutionary Computation, 5(4), 373–399

    Google Scholar 

  15. Fletcher, J. A., and Zwick, M., 1996, Dependence of Adaptability on Environmental Structure in a Simple Evolutionary Model, Adaptive Behavior, vol 4, 3/4, 283–315

    Google Scholar 

  16. Menczer, F., and Belew, R. K., 1996, From Complex Environments to Complex Behaviors, Adaptive Behavior, vol 4, 3/4, 317–363

    Google Scholar 

  17. Phelps, S. M. and Ryan, M. J., 2000, History influences signal recognition: neural network models of tungara frogs, Proc. Royal Society London B, 267, 1633–1639

    Google Scholar 

  18. Ryan, M. J., Phelps, S. M., and Rand, A. S., 2001, How evolutionary history shapes recognition mechanisms, Trends in Cognitive Sciences, vol 5, 4, 143–148

    Google Scholar 

  19. Edmonds, I. R., 2001, Tracking the Evolution of a Foraging Agent, Technical Report, SBU-CISM-01-07, South Bank University, London

    Google Scholar 

  20. Gelenbe, E., Schmajuk, N., Staddon, J., and Rief, J., 1997, Autonomous search by robots and animals: A survey, Robotics and Autonomous Systems, 22, 23–34

    Google Scholar 

  21. Milne, B. T., 1991, Lessons from Applying Fractal Models to Landscape Patterns, in Turner, M. G., and Gardner, R. H., (eds), Quantitative Methods in Landscape Ecology, Springer-Verlag, 199–235

    Google Scholar 

  22. Tikhonov, D. A., Enderlein, J., Malchow, H., and Medvinsky, A. B., 2001, Chaos and fractals in fish school motion, Chaos, Solitons and Fractals 12, 277–288

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Edmonds, I.R. (2002). The Impact of Environmental Structure on the Evolutionary Trajectories of a Foraging Agent. In: Collet, P., Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2001. Lecture Notes in Computer Science, vol 2310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46033-0_27

Download citation

  • DOI: https://doi.org/10.1007/3-540-46033-0_27

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43544-0

  • Online ISBN: 978-3-540-46033-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics