Malone, T. W. & Bernstein, M. S. (eds) Handbook of Collective Intelligence (MIT Press, 2022).
Flack, J., Ipeirotis, P., Malone, T. W., Mulgan, G. & Page, S. E. Editorial to the inaugural issue of Collective Intelligence. Collect. Intell. 1, 26339137221114179 (2022).
Google Scholar
Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H. & Karamanoglu, M. (eds) Swarm Intelligence and Bio-Inspired Computation: Theory and Applications (Elsevier, 2013).
Fister, I. Jr, Yang, X.-S., Fister, I., Brest, J. & Fister, D. A brief review of nature-inspired algorithms for optimization. Elektroteh. Vest. 80, 116–122 (2013).
Cox, D. D. & Dean, T. Neural networks and neuroscience-inspired computer vision. Curr. Biol. 24, R921–R929 (2014).
Google Scholar
Chakraborty, A. & Kar, A. K. in Nature-Inspired Computing and Optimization: Theory and Applications (eds Patnaik, S. et al.) 475–494 (Springer, 2017).
Schmidgall, S. et al. Brain-inspired learning in artificial neural networks: a review. APL Mach. Learn. 2, 021501 (2024).
Google Scholar
Webb, B. Can robots make good models of biological behaviour? Behav. Brain Sci. 24, 1033–1050 (2001).
Google Scholar
Reynolds, C. W. Flocks, herds and schools: a distributed behavioral model. In Proc. 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) 21, 25–34 (ACM, 1987).
Cavagna, A. et al. Marginal speed confinement resolves the conflict between correlation and control in collective behaviour. Nat. Commun. 13, 2315 (2022).
Google Scholar
Solé, R., Moses, M. & Forrest, S. Liquid brains, solid brains. Phil. Trans. R. Soc. B 374, 20190040 (2019).
Google Scholar
Seung, S. Connectome: How the Brain’s Wiring Makes Us Who We Are (Houghton Mifflin Harcourt, 2012).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Google Scholar
Vining, W. F., Esponda, F., Moses, M. E. & Forrest, S. How does mobility help distributed systems compute? Phil. Trans. R. Soc. B 374, 20180375 (2019).
Google Scholar
Mitchell, M., Crutchfield, J. P. & Das, R. Evolving cellular automata with genetic algorithms: a review of recent work. In Proc. First International Conference on Evolutionary Computation and its Applications (EvCA’96) (Presidium of the Russian Academy of Sciences, 1996).
Beal, J. & Bachrach, J. Infrastructure for engineered emergence on sensor/actuator networks. IEEE Intell. Syst. 21, 10–19 (2006).
Google Scholar
Shapiro, J. A. Thinking about bacterial populations as multicellular organisms. Annu. Rev. Microbiol. 52, 81–104 (1998).
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Google Scholar
Churchland, P. S. & Sejnowski, T. J. The Computational Brain (MIT Press, 2016).
Toffoli, T. & Margolus, N. Cellular Automata Machines: A New Environment for Modeling (MIT Press, 1987).
Morris, R. L. & Miller, J. R. (eds) Designing with TTL Integrated Circuits (McGraw-Hill, 1971).
Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory (Wiley, 1949).
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).
Google Scholar
Piqueret, B., Sandoz, J.-C. & d’Ettorre, P. Ants learn fast and do not forget: associative olfactory learning, memory and extinction in Formica fusca. R. Soc. Open Sci. 6, 6190778 (2019).
Google Scholar
Busoniu, L., Babuska, R. & De Schutter, B. A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. C 38, 156–172 (2008).
Google Scholar
von Frisch, K. The dances of the honey bee. Bull. Anim. Behav. 5, 1–32 (1947).
Wenner, A. M. & Wells, P. H. Anatomy of a Controversy: The Question of a ‘Language’ Among Bees (Columbia Univ. Press, 1990).
Riley, J. R., Greggers, U., Smith, A. D., Reynolds, D. R. & Menzel, R. The flight paths of honeybees recruited by the waggle dance. Nature 435, 205–207 (2005).
Google Scholar
Bredeche, N. & Fontbonne, N. Social learning in swarm robotics. Phil. Trans. R. Soc. B 377, 20200309 (2022).
Google Scholar
Lyu, X., Baisero, A., Xiao, Y., Daley, B. & Amato, C. On centralized critics in multi-agent reinforcement learning. J. Artif. Intell. Res. 77, 295–354 (2023).
Google Scholar
Lowe, R. et al. Multi-agent actor-critic for mixed cooperative-competitive environments. In Proc. 31st Conference on Neural Information Processing Systems (NIPS) (eds Guyin, I. et al.) (NeurIPS, 2017).
Tolstaya, E. et al. Learning decentralized controllers for robot swarms with graph neural networks. In Proc. Conference on Robot Learning (CoRL) 671–682 (PMLR, 2020).
Jaderberg, M. et al. Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science 364, 859–865 (2019).
Google Scholar
Destexhe, A. & Marder, E. Plasticity in single neuron and circuit computations. Nature 431, 789–795 (2004).
Google Scholar
Legenstein, R. & Maass, W. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. J. Neurosci. 31, 10787–10802 (2011).
Google Scholar
Bower, J. M. & Beeman, D. The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System (Springer, 1998).
Jones, I. S. & Kording, K. P. Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? Neur. Comput. 33, 1554–1571 (2021).
Google Scholar
Acharya, J. et al. Dendritic computing: branching deeper into machine learning. Neuroscience 489, 275–289 (2022).
Google Scholar
Spieler, A., Rahaman, N., Martius, G., Schölkopf, B. & Levina, A. The expressive leaky memory neuron: an efficient and expressive phenomenological neuron model can solve long-horizon tasks. In Proc. Twelfth International Conference on Learning Representations (OpenReview, 2024).
Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).
Google Scholar
Zheng, M., Kashimori, Y., Hoshino, O., Fujita, K. & Kambara, T. Behavior pattern (innate action) of individuals in fish schools generating efficient collective evasion from predation. J. Theor. Biol. 235, 153–167 (2005).
Google Scholar
Bartashevich, P. et al. Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies. Commun. Biol. 7, 1586 (2024).
Google Scholar
Magurran, A. E. & Pitcher, T. J. Provenance, shoal size and the sociobiology of predator-evasion behaviour in minnow shoals. Proc. R. Soc. London B 229, 439–465 (1987).
Google Scholar
Storms, R. F., Carere, C., Zoratto, F. & Hemelrijk, C. K. Complex patterns of collective escape in starling flocks under predation. Behav. Ecol. Sociobiol. 73, 10 (2019).
Google Scholar
Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).
Google Scholar
Biro, D., Sumpter, D. J., Meade, J. & Guilford, T. From compromise to leadership in pigeon homing. Curr. Biol. 16, 2123–2128 (2006).
Google Scholar
Dyer, J. R. et al. Consensus decision making in human crowds. Anim. Behav. 75, 461–470 (2008).
Google Scholar
Joshi, V., Popp, S., Werfel, J. & McCreery, H. F. Alignment with neighbours enables escape from dead ends in flocking models. J. R. Soc. Interface 19, 20220356 (2022).
Google Scholar
Zhu, W. et al. Self-organizing nervous systems for robot swarms. Sci. Rob. 9, eadl5161 (2024).
Google Scholar
Daymude, J. J., Richa, A. W. & Scheideler, C. Local mutual exclusion for dynamic, anonymous, bounded memory message passing systems. In Proc. 1st Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2022) (eds Aspnes, J. & Michail, O.) 12:1–12:19 (Dagstuhl, 2022).
Grassé, P.-P. La reconstruction du nid et les coordinations inter-individuelles chez Bellicositermes natalensis et Cubitermes sp. La théorie de la stigmergie: essai d’interpretation du comportement des termites constructeurs. Insect. Soc. 6, 41–81 (1959).
Google Scholar
Heylighen, F. Stigmergy as a universal coordination mechanism I: definition and components. Cognit. Syst. Res. 38, 4–13 (2016).
Google Scholar
David Morgan, E. Trail pheromones of ants. Physiol. Entomol. 34, 1–17 (2009).
Google Scholar
Bordereau, C. & Pasteels, J. M. in Biology of Termites: A Modern Synthesis (eds Bignell, D. E. et al.) 279–320 (Springer, 2011).
Whiteley, M., Diggle, S. P. & Greenberg, E. P. Progress in and promise of bacterial quorum sensing research. Nature 551, 313–320 (2017).
Google Scholar
Barja, I. et al. Evaluating the functional, sexual and seasonal variation in the chemical constituents from feces of adult Iberian wolves (Canis lupus signatus). Sci. Rep. 13, 6669 (2023).
Google Scholar
Kleiner, A., Prediger, J. & Nebel, B. RFID technology-based exploration and SLAM for search and rescue. In Proc. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 4054–4059 (IEEE, 2006).
Wenzel, J. W. in Encyclopedia of Social Insects (ed. Starr, C. K. et al.) 1–14 (Springer, 2020).
Smith, M. L., Napp, N. & Petersen, K. H. Imperfect comb construction reveals the architectural abilities of honeybees. Proc. Natl. Acad. Sci. USA 118, e2103605118 (2021).
Google Scholar
Calovi, D. S. et al. Surface curvature guides early construction activity in mound-building termites. Phil. Trans. R. Soc. B 374, 20180374 (2019).
Google Scholar
Kennedy, J. et al. Environmental cues influence timing and location of construction activity in a beaver damming complex. Preprint at bioRxiv https://doi.org/10.1101/2024.10.21.619304 (2024).
Franks, N., Wilby, A., Silverman, B. W. & Tofts, C. Self-organizing nest construction in ants: sophisticated building by blind bulldozing. Anim. Behav. 44, 357–375 (1992).
Google Scholar
Karsai, I. & Pénzes, Z. Comb building in social wasps: self-organization and stigmergic script. J. Theor. Biol. 161, 505–525 (1993).
Google Scholar
Theraulaz, G. & Bonabeau, E. Coordination in distributed building. Science 269, 686–688 (1995).
Google Scholar
Ladley, D. & Bullock, S. The role of logistic constraints in termite construction of chambers and tunnels. J. Theor. Biol. 234, 551–564 (2005).
Google Scholar
Khuong, A. et al. Stigmergic construction and topochemical information shape ant nest architecture. Proc. Natl. Acad. Sci. USA 113, 1303–1308 (2016).
Google Scholar
Harwell, J. & Gini, M. Swarm engineering through quantitative measurement of swarm robotic principles in a 10,000 robot swarm. In Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI) (ed. Kraus, S.) 336–342 (AAAI Press, 2019).
Parker, C. A. C., Zhang, H. & Kube, C. R. Blind bulldozing: multiple robot nest construction. In Proc. 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2010–2015 (IEEE, 2003).
Werfel, J., Petersen, K. & Nagpal, R. Designing collective behavior in a termite-inspired robot construction team. Science 343, 754–758 (2014).
Google Scholar
Aguilar, J. et al. Collective clog control: optimizing traffic flow in confined biological and robophysical excavation. Science 361, 672–677 (2018).
Google Scholar
Petersen, K. H., Napp, N., Stuart-Smith, R., Rus, D. & Kovac, M. A review of collective robotic construction. Sci. Rob. 4, eaau8479 (2019).
Google Scholar
Dennett, D. Elbow Room: The Varieties of Free Will Worth Wanting (MIT Press, 1984).
Hofstadter, D. R. in Metamagical Themas 1st edn, 526–546 (Basic Books, 1985).
Brockmann, H. J. Provisioning behavior of the great golden digger wasp, Sphex ichneumoneus (L.) (Specidae). J. Kansas Entomol. Soc. 58, 631–655 (1985).
Keijzer, F. The Sphex story: how the cognitive sciences kept repeating an old and questionable anecdote. Phil. Psychol. 26, 502–519 (2013).
Google Scholar
Czaczkes, T. J. & Ratnieks, F. L. W. Simple rules result in the adaptive turning of food items to reduce drag during cooperative food transport in the ant Pheidole oxyops. Insect. Soc. 58, 91–96 (2011).
Google Scholar
Feinerman, O., Pinkoviezky, I., Gelblum, A., Fonio, E. & Gov, N. S. The physics of cooperative transport in groups of ants. Nat. Phys. 14, 683–693 (2018).
Google Scholar
Wang, Z. & Schwager, M. Force-amplifying n-robot transport system (Force-ANTS) for cooperative planar manipulation without communication. Int. J. Rob. Res. 35, 1564–1586 (2016).
Google Scholar
Carey, N. E. & Werfel, J. Collective transport of unconstrained objects via implicit coordination and adaptive compliance. In Proc. 2021 IEEE International Conference on Robotics and Automation (ICRA) 12603–12609 (IEEE, 2021).
Carey, N. E. & Werfel, J. A force-mediated controller for cooperative object manipulation with independent autonomous robots. In Proc. 16th International Symposium on Distributed Autonomous Robotic Systems (DARS 2022) (eds Bourgeois, J. et al.) 140–155 (Springer, 2024).
Zhang, H., Liu, C. L., Elwin, M. L., Freeman, R. A. & Lynch, K. M. Cooperative payload estimation by a team of mocobots. IEEE Robot. Autom. Lett. 10, 9806–9813 (2025).
Google Scholar
Chen, J., Gauci, M., Li, W., Kolling, A. & Groß, R. Occlusion-based cooperative transport with a swarm of miniature mobile robots. IEEE Trans. Rob. 31, 307–321 (2015).
Google Scholar
Eppner, C., Deimel, R., Alvarez-Ruiz, J., Maertens, M. & Brock, O. Exploitation of environmental constraints in human and robotic grasping. Int. J. Rob. Res. 34, 1021–1038 (2015).
Google Scholar
Boudet, J. F. et al. From collections of independent, mindless robots to flexible, mobile, and directional superstructures. Sci. Rob. 6, eabd0272 (2021).
Google Scholar
Zion, M. Y. B., Fersula, J., Bredeche, N. & Dauchot, O. Morphological computation and decentralized learning in a swarm of sterically interacting robots. Sci. Rob. 8, eabo6140 (2023).
Google Scholar
Kaeser, C. et al. Individual and collective behaviors in soft robot worms inspired by living worm blobs. In Proc. IEEE International Conference on Robotics and Automation (ICRA) 2577–2583 (IEEE, 2025).
Savoie, W. et al. A robot made of robots: emergent transport and control of a smarticle ensemble. Sci. Rob. 4, eaax4316 (2019).
Google Scholar
Ozkan-Aydin, Y., Goldman, D. I. & Bhamla, M. S. Collective dynamics in entangled worm and robot blobs. Proc. Natl. Acad. Sci. USA 118, e2010542118 (2021).
Google Scholar
Abad, S.-A., Sornkarn, N. & Nanayakkara, T. The role of morphological computation of the goat hoof in slip reduction. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 5599–5605 (IEEE, 2016).
Teeple, C. B., Werfel, J., & Wood, R. J. Multi-dimensional compliance of soft grippers enables gentle interaction with thin, flexible objects. In Proc. IEEE International Conference on Robotics and Automation (ICRA) 728–734 (IEEE, 2022).
Melenbrink, N., Teeple, C. & Werfel, J. A robot factors approach to designing modular hardware. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 3528–3535 (IEEE, 2022).
Araque, A. & Navarrete, M. Glial cells in neuronal network function. Phil. Trans. R. Soc. B 365, 2375–2381 (2010).
Google Scholar
Perea, G., Sur, M. & Araque, A. Neuron-glia networks: integral gear of brain function. Front. Cell. Neurosci. 8, 378 (2014).
Google Scholar
Hsiao, K. et al. Correlative memory deficits, Aβ elevation, and amyloid plaques in transgenic mice. Science 274, 99–103 (1996).
Google Scholar
Cacucci, F., Yi, M., Wills, T. J., Chapman, P. & O’Keefe, J. Place cell firing correlates with memory deficits and amyloid plaque burden in Tg2576 Alzheimer mouse model. Proc. Natl. Acad. Sci. USA 105, 7863–7868 (2008).
Google Scholar
Bubeck, S. & Sellke, M. A universal law of robustness via isoperimetry. J. ACM 70, 1–18 (2023).
Google Scholar
Mozer, M. C. & Smolensky, P. Using relevance to reduce network size automatically. Connect. Sci. 1, 3–16 (1989).
Google Scholar
Liu, Z. et al. Learning efficient convolutional networks through network slimming. In Proc. IEEE International Conference on Computer Vision (ICCV) 2755–2763 (IEEE, 2017).
Arjun, D., Indukala, P. & Menon, K. U. Border surveillance and intruder detection using wireless sensor networks: a brief survey. In Proc. International Conference on Communication and Signal Processing (ICCSP) 1125–1130 (IEEE, 2017).
Rybski, P. E. et al. Enlisting rangers and scouts for reconnaissance and surveillance. IEEE Rob. Autom. Mag. 7, 14–24 (2000).
Google Scholar
Liu, B., Dousse, O., Nain, P. & Towsley, D. Dynamic coverage of mobile sensor networks. IEEE Trans. Parall. Distrib. Syst. 24, 301–311 (2013).
Google Scholar
Xiao, L., Boyd, S. & Kim, S.-J. Distributed average consensus with least-mean-square deviation. In Proc. 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS) 2768–2776 (MTNS, 2006).
Valentini, G., Brambilla, D., Hamann, H. & Dorigo, M. Collective perception of environmental features in a robot swarm. In Proc. 10th International Conference on Swarm Intelligence (ANTS) (eds Dorigo, M. et al.) 65–76 (Springer, 2016).
Ebert, J. T., Gauci, M. & Nagpal, R. Multi-feature collective decision making in robot swarms. In Proc. 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 1711–1719 (IFAAMAS, 2018).
Guyeux, C., Haddad, M., Hakem, M. & Lagacherie, M. Efficient distributed average consensus in wireless sensor networks. Comput. Commun. 150, 115–121 (2019).
Google Scholar
Bose, T., Reina, A. & Marshall, J. A. Collective decision-making. Curr. Opin. Behav. Sci. 16, 30–34 (2017).
Google Scholar
LeCun, Y. et al. Handwritten digit recognition with a back-propagation network. In Proc. 3rd International Conference on Neural Information Processing Systems (NIPS) (ed. Touretzky, D.) 396–404 (Morgan Kaufmann, 1989).
Deng, L. The MNIST database of handwritten digit images for machine learning research [Best of the Web]. IEEE Signal Process. Mag. 29, 141–142 (2012).
Google Scholar
Aberer, K. et al. Opensense: open community driven sensing of environment. In Proc. ACM SIGSPATIAL International Workshop on GeoStreaming (eds Ali, M. et al.) 39–42 (ACM, 2010).
Di Francesco, M., Das, S. K. & Anastasi, G. Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans. Sensor Networks 8, 1–31 (2011).
Google Scholar
Guo, Y., Xu, Z. & Saleh, J. Collaborative allocation and optimization of path planning for static and mobile sensors in hybrid sensor networks for environment monitoring and anomaly search. Sensors 21, 7867 (2021).
Google Scholar
Dorkenwald, S. et al. Neuronal wiring diagram of an adult brain. Nature 634, 124–138 (2024).
Google Scholar
Smolders, S. M.-T. et al. Microglia: brain cells on the move. Prog. Neurobiol. 178, 101612 (2019).
Google Scholar
Nimmerjahn, A., Kirchhoff, F. & Helmchen, F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 308, 1314–1318 (2005).
Google Scholar
Parkhurst, C. N. et al. Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013).
Google Scholar
Khona, M., Chandra, S., Ma, J. J. & Fiete, I. R. Winning the lottery with neural connectivity constraints: faster learning across cognitive tasks with spatially constrained sparse RNNs. Neur. Comput. 35, 1850–1869 (2023).
Google Scholar
Shaw, E. Schooling fishes: the school, a truly egalitarian form of organization in which all members of the group are alike in influence, offers substantial benefits to its participants. Am. Sci. 66, 166–175 (1978).
Nagy, M., Ákos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).
Google Scholar
Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).
Google Scholar
Nowak, M. A., Tarnita, C. E. & Antal, T. Evolutionary dynamics in structured populations. Phil. Trans. R. Soc. Lond. B 365, 19–30 (2010).
Google Scholar
Werfel, J., Ingber, D. E. & Bar-Yam, Y. Programed death is favored by natural selection in spatial systems. Phys. Rev. Lett. 114, 238103 (2015).
Google Scholar
Prindle, A. et al. Ion channels enable electrical communication in bacterial communities. Nature 527, 59–63 (2015).
Google Scholar
Rainey, P. B. & Rainey, K. Evolution of cooperation and conflict in experimental bacterial populations. Nature 425, 72–74 (2003).
Google Scholar
Berdahl, A., Torney, C. J., Ioannou, C. C., Faria, J. & Couzin, I. D. Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013).
Google Scholar
Flocchini, P., Prencipe, G. & Santoro, N. (eds) Distributed Computing by Mobile Entities: Current Research in Moving and Computing (Springer, 2019).
Weber, J. W., Chhabra, T., Richa, A. W. & Daymude, J. J. Energy-constrained programmable matter under unfair adversaries. In Proc. 27th International Conference on Principles of Distributed Systems (OPODIS) (eds Bessani, A. et al.) 7:1–7:21 (Dagstuhl, 2024).
Daymude, J. J., Richa, A. W. & Weber, J. W. Bio-inspired energy distribution for programmable matter. In Proc. 22nd International Conference on Distributed Computing and Networking (ICDCN) 86–95 (ACM, 2021).
Werfel, J., Bar-Yam, Y. & Nagpal, R. Building patterned structures with robot swarms. In Proc. 19th International Joint Conference on Artificial Intelligence (IJCAI) 1495–1502 (Morgan Kaufmann, 2005).
Tolley, M. T., Kalontarov, M., Neubert, J., Erickson, D. & Lipson, H. Stochastic modular robotic systems: a study of fluidic assembly strategies. IEEE Trans. Rob. 26, 518–530 (2010).
Google Scholar
Haghighat, B. & Martinoli, A. Automatic synthesis of rulesets for programmable stochastic self-assembly of rotationally symmetric robotic modules. Swarm Intell. 11, 243–270 (2017).
Google Scholar
Wang, W. et al. Active rendezvous for multi-robot pose graph optimization using sensing over Wi-Fi. In Proc. 19th International Symposium on Robotics Research (ISRR 2019) (eds Asfour, T. et al.) 832–849 (Springer, 2022).
Hoff, N. R., Sagoff, A., Wood, R. J. & Nagpal, R. Two foraging algorithms for robot swarms using only local communication. In Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO) 123–130 (IEEE, 2010).
Rubenstein, M., Cornejo, A. & Nagpal, R. Programmable self-assembly in a thousand-robot swarm. Science 345, 795–799 (2014).
Google Scholar
Dobzhansky, T. Nothing in biology makes sense except in the light of evolution. Am. Biol. Teacher 35, 125–129 (1973).
Google Scholar
Werfel, J. jkwerfel17/FluidThinking: NMIP code/data. Zenodo https://doi.org/10.5281/zenodo.18684944 (2026).
Arnaouteli, S., Bamford, N. C., Stanley-Wall, N. R. & Kovács, Á. T. Bacillus subtilis biofilm formation and social interactions. Nat. Rev. Microbiol. 19, 600–614 (2021).
Google Scholar
Peak, D., West, J. D., Messinger, S. M. & Mott, K. A. Evidence for complex, collective dynamics and emergent, distributed computation in plants. Proc. Natl Acad. Sci. USA 101, 918–922 (2004).
Google Scholar
Ramírez-Ávila, G. M., Kurths, J. & Deneubourg, J. L. in Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives (eds Edelman, M. et al.) 35–64 (Springer, 2018).
Abelson, H. et al. Amorphous computing. Commun. ACM 43, 74–82 (2001).
Google Scholar
Warneke, B., Last, M., Liebowitz, B. & Pister, K. Smart dust: communicating with a cubic-millimeter computer. Computer 34, 44–51 (2001).
Google Scholar
Lioni, A., Sauwens, C., Theraulaz, G. & Deneubourg, J.-L. Chain formation in Oecophylla longinoda. J. Insect Behav. 14, 679–696 (2001).
Google Scholar
Loomis, W. F. Genetic control of morphogenesis in Dictyostelium. Dev. Biol. 402, 146–161 (2015).
Google Scholar
Zhang, H. P., Be’er, A., Florin, E.-L. & Swinney, H. L. Collective motion and density fluctuations in bacterial colonies. Proc. Natl Acad. Sci. USA 107, 13626–13630 (2010).
Google Scholar
Liu, C., Lin, Q., Kim, H. & Yim, M. SMORES-EP, a modular robot with parallel self-assembly. Auton. Robot. 47, 211–228 (2023).
Google Scholar
Berlinger, F., Gauci, M. & Nagpal, R. Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm. Sci. Robot. 6, eabd8668 (2021).
Google Scholar
Sumpter, D. J. T. & Beekman, M. From nonlinearity to optimality: pheromone trail foraging by ants. Anim. Behav. 66, 273–280 (2003).
Google Scholar
Edelen, M. R. Swarm Intelligence and Stigmergy: Robotic Implementation of Foraging Behavior. MSc thesis, Univ. Maryland (2003).
Jennings, J., Whelan, G. & Evans, W. Cooperative search and rescue with a team of mobile robots. In Proc. 8th International Conference on Advanced Robotics (ICAR) 193–200 (IEEE, 1997).
Seeley, T. D. Honeybee Democracy (Princeton Univ. Press, 2010).
Gross, R., Bonani, M., Mondada, F. & Dorigo, M. Autonomous self-assembly in swarm-bots. IEEE Trans. Robot. 22, 1115–1130 (2006).
Google Scholar
Østergaard, E. H., Kassow, K., Beck, R. & Lund, H. H. Design of the ATRON lattice-based self-reconfigurable robot. Auton. Robot. 21, 165—183 (2006).
Google Scholar
Garnier, S., Combe, M., Jost, C. & Theraulaz, G. Do ants need to estimate the geometrical properties of trail bifurcations to find an efficient route? A swarm robotics test bed. PLoS Comput. Biol. 9, e1002903 (2013).
Google Scholar
Napp, N. & Klavins, E. Load balancing for multi-robot construction. In Proc. IEEE International Conference on Robotics and Automation 254–260 (IEEE, 2011).
Oliveira, H. M. & Melo, L. V. Huygens synchronization of two clocks. Sci. Rep. 5, 11548 (2015).
Google Scholar
Champlin, J. D. & Bostwick, A. E. in The Young Folk’s Cyclopædia of Games and Sports 404–406 (Henry Holt, 1890).
McLurkin, J. et al. A robot system design for low-cost multi-robot manipulation. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 912–918 (IEEE, 2014).
