Abstract
Maze solving and finding the shortest path or all possible exit paths in mazes can be interpreted as mathematical problems which can be solved algorithmically. These algorithms can be used by both living entities (such as humans, animals, cells) and non-living systems (computer programs, simulators, robots, particles). In this chapter we summarize several chemistry-based concepts for maze solving in two-dimensional standard mazes which rely on surface tension driven phenomena at the air-liquid interface. We show that maze solving can be implemented by using: (i) active (self-propelled) droplets and/or (ii) passive particles (chemical entities).
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References
A. Adamatzky, Hot ice computer. Phys. Lett. A 374, 264–271 (2009)
A. Adamatzky, Slime mold solves maze in one pass, assisted by gradient of chemo-attractants. IEEE Trans. Nanobiosci. 11, 131–134 (2012)
A. Adamatzky, Physical maze solvers. All twelve prototypes implement 1961 Lee algorithm, in Emergent Computation: A Festschrift for Selim G. Akl, ed. by A. Adamatzky (Cham, Springer International Publishing, 2017), pp. 489–504
A. Braun, R. Tóth, I. Lagzi, Künstliche Intelligenz aus dem Chemiereaktor. Nachr. Chem. 63, 445–446 (2015)
J. Čejková, M. Novák, F. Štěpánek, M.M. Hanczyc, Dynamics of chemotactic droplets in salt concentration gradients. Langmuir 30, 11937–11944 (2014)
J. Čejková, T. Banno, F. Štěpánek, M.M. Hanczyc, Droplets as liquid robots. Artif. Life 23, 528–549 (2017)
J. Čejková, S. Holler, N.T. Quyen, C. Kerrigan, F. Štěpánek, M.M. Hanczyc, Chemotaxis and chemokinesis of living and non-living objects, in Advances in Unconventional Computing, ed. by A. Adamatzky (Springer, 2017), pp. 245–260
A.E. Dubinov, A.N. Maksimov, M.S. Mironenko, N.A. Pylayev, V.D. Selemir, Glow discharge based device for solving mazes. Phys. Plasmas 21, 093503 (2014)
M.J. Fuerstman, P. Deschatelets, R. Kane, A. Schwartz, P.J.A. Kenis, J.M. Deutch, G.M. Whitesides, Solving mazes using microfluidic networks. Langmuir 19, 4714–4722 (2003)
I. Lagzi, S. Soh, P.J. Wesson, K.P. Browne, B.A. Grzybowski, Maze solving by chemotactic droplets. J. Am. Chem. Soc. 132, 1198–1199 (2010)
P. Lovass, M. Branicki, R. Tóth, A. Braun, K. Suzuno, D. Ueyama, I. Lagzi, Maze solving using temperature-induced Marangoni flow. RSC Adv. 5, 48563–48568 (2015)
T. Nakagaki, H. Yamada, A. Tóth, Maze-solving by an amoeboid organism. Nature 407, 470–470 (2000)
T. Nakagaki, H. Yamada, A. Tóth, Path finding by tube morphogenesis in an amoeboid organism. Biophys. Chem. 92, 47–52 (2001)
Y.V. Pershin, M. Di Ventra, Solving mazes with memristors: a massively parallel approach. Phys. Rev. E 84, 046703 (2011)
D.R. Reyes, M.M. Ghanem, G.M. Whitesides, A. Manz, Glow discharge in microfluidic chips for visible analog computing. Lab Chip 2, 113–116 (2002)
O. Steinbock, A. Tóth, K. Showalter, Navigating complex labyrinths: optimal paths from chemical waves. Science 267, 868–871 (1995)
O. Steinbock, P. Kettunen, K. Showalter, Chemical wave logic gates. J. Phys. Chem. 100, 18970–18975 (1996)
K. Suzuno, D. Ueyama, M. Branicki, R. Tóth, A. Braun, I. Lagzi, Maze solving using fatty acid chemistry. Langmuir 30, 9251–9255 (2014)
Y. Yu, G. Pan, Y. Gong, K. Xu, N. Zheng, W. Hua, X. Zheng, Z. Wu, Intelligence-augmented rat cyborgs in maze solving. PLoS ONE 11, e014775 (2016)
Acknowledgements
J. Č. was financially supported by the Czech Science Foundation (Grant No. 17-21696Y). Other authors acknowledge the financial support of the Hungarian Research Fund (OTKA K104666). Financial support for R. T. by the Marie Heim-Vogtlin Program under project no PMPDP2-139698 is gratefully acknowledged. D. U. and I. L. gratefully acknowledge the financial support of the National Research, Development and Innovation Office of Hungary (TÉT12JP-1-2014-0005).
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Čejková, J., Tóth, R., Braun, A., Branicki, M., Ueyama, D., Lagzi, I. (2018). Shortest Path Finding in Mazes by Active and Passive Particles. In: Adamatzky, A. (eds) Shortest Path Solvers. From Software to Wetware. Emergence, Complexity and Computation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-77510-4_15
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DOI: https://doi.org/10.1007/978-3-319-77510-4_15
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