Swarm Intelligence

, Volume 5, Issue 3–4, pp 257–281 | Cite as

Functional blueprints: an approach to modularity in grown systems

Article

Abstract

The engineering of grown systems poses fundamentally different system integration challenges than ordinary engineering of static designs. On the one hand, a grown system must be capable of surviving not only in its final form, but at every intermediate stage, despite the fact that its subsystems may grow unevenly or be subject to different scaling laws. On the other hand, the ability to grow offers much greater potential for adaptation, either to changes in the environment or to internal stresses developed as the system grows. I observe that the ability of subsystems to tolerate stress can be used to transform incremental adaptation into the dynamic discovery of viable growth trajectories for the system as a whole. Using this observation, I propose an engineering approach based on functional blueprints, under which a system is specified in terms of desired performance and means of incrementally correcting deficiencies. I explore how manifold geometric programming can support such an approach by simplifying the construction of distortion-tolerant programs, then demonstrate the functional blueprints approach by applying it to integrate simplified models of tissue growth and vascularization, and further show how the composed system may itself be modulated for use as a component in a more complex design.

Keywords

Morphogenetic engineering Spatial computing Amorphous computing Functional blueprints Proto 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11721_2011_56_MOESM1_ESM.pdf (37.5 mb)
(PDF 37.5 MB)

References

  1. Aubin, J. P. (1991). Viability theory. Basel: Birkhauser. MATHGoogle Scholar
  2. Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H., & Weiss, R. (2005). A synthetic multicellular systems for programmed pattern formation. Nature, 434, 1130–1134. CrossRefGoogle Scholar
  3. Beal, J. (2004). Programming an amorphous computational medium. In Lecture notes in computer science: Vol. 3566. Unconventional programming paradigms international workshop (pp. 121–136). Berlin: Springer. CrossRefGoogle Scholar
  4. Beal, J. (2009). Flexible self-healing gradients. In ACM symposium on applied computing (pp. 1197–1201). New York: ACM. CrossRefGoogle Scholar
  5. Beal, J., & Bachrach, J. (2006). Infrastructure for engineered emergence in sensor/actuator networks. IEEE Intelligent Systems, 21, 10–19. Google Scholar
  6. Beal, J., Bachrach, J., Vickery, D., & Tobenkin, M. (2008). Fast self-healing gradients. In ACM symposium on applied computing. New York: ACM. Google Scholar
  7. Carmeliet, P. (2003). Angiogenesis in health and disease. Nature Medicine, 9(6), 653–660. CrossRefGoogle Scholar
  8. Carroll, S. B. (2005). Endless forms most beautiful: the new science of evo devo and the making of the animal kingdom. New York: W.W. Norton & Company. Google Scholar
  9. Coore, D. (1999). Botanical computing: a developmental approach to generating inter connect topologies on an amorphous computer. Ph.D. thesis, MIT, Cambridge, MA, USA. Google Scholar
  10. Couzin, I., Krause, J., Franks, N., & Levin, S. (2005). Effective leadership and decision making in animal groups on the move. Nature, 433, 513–516. CrossRefGoogle Scholar
  11. Dorigo, M., & Birattari, M. (2007). Swarm intelligence. Scholarpedia, 2(9), 1462. CrossRefGoogle Scholar
  12. Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. Cambridge: MIT Press. CrossRefMATHGoogle Scholar
  13. Doursat, R. (2008). The growing canvas of biological development: multiscale pattern generation on an expanding lattice of gene regulatory networks. In Proceedings of the 6th international conference on complex systems: Vol. 6. Unifying themes in complex systems. New York: Springer. Google Scholar
  14. Kirschner, M. W., & Norton, J. C. (2005). The plausibility of life: resolving Darwin’s dilemma. New Haven: Yale University Press. Google Scholar
  15. Kondacs, A. (2003). Biologically-inspired self-assembly of 2d shapes, using global-to-local compilation. In International joint conference on artificial intelligence (IJCAI). Menlo Park: AAAI. Google Scholar
  16. MIT Proto (Retrieved November 22, 2010). MIT Proto. Software available at http://proto.bbn.com/.
  17. Nagpal, R. (2001). Programmable self-assembly: constructing global shape using biologically-inspired local interactions and origami mathematics. Ph.D. thesis, MIT, Cambridge, MA, USA. Google Scholar
  18. O’Grady, R., Christensen, A., & Dorigo, M. (2009). Swarmorph: multi-robot morphogenesis using directional self-assembly. IEEE Transactions on Robotics, 25(3), 738–743. CrossRefGoogle Scholar
  19. O’Grady, R., Christensen, AL, Pinciroli, C., & Dorigo, M. (2010). Robots autonomously self-assemble into dedicated morphologies to solve different tasks. In Proceedings of 9th international conference on autonomous agents and multiagent systems (AAMAS 2010) (pp. 1517–1518). Toronto: IFAAMAS. Google Scholar
  20. Prusinkiewicz, P., & Lindenmayer, A. (1990). The algorithmic beauty of plants. New York: Springer. CrossRefMATHGoogle Scholar
  21. Reynolds, C. (1987). Flocks herds, and schools: a distributed behavioral model. Computer Craphics (SIGGRAPH’87 Conference Proceedings), 21(4), 25–34. MathSciNetGoogle Scholar
  22. Shetty, R. P., Endy, D., Thomas, F., & Knight, J. (2008). Engineering biobrick vectors from biobrick parts. Journal of Biological Engineering, 2(5). Google Scholar
  23. Spicher, A., & Michel, O. (2006). Declarative modeling of a neurulation-like process. BioSystems, 87, 281–288. CrossRefGoogle Scholar
  24. Stoy, K., & Nagpal, R. (2004). Self-reconfiguration using directed growth. In Intl. symposium on distributed autonomous robotic systems (DARS). New York: Springer. Google Scholar
  25. Werfel, J. (2006). Anthills built to order: automating construction with artificial swarms. Ph.D. thesis, MIT, Cambridge, MA, USA. Google Scholar
  26. Werfel, J., & Nagpal, R. (2007). Collective construction of environmentally-adaptive structures. In 2007 IEEE/RSJ international conference on intelligent robots and systems (IROS 2007). Piscataway: IEEE. Google Scholar
  27. Yamins, D. (2007). A theory of local-to-global algorithms for one-dimensional spatial multi-agent systems. Ph.D. thesis, Harvard, Cambridge, MA, USA. Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  1. 1.BBN TechnologiesCambridgeUSA

Personalised recommendations