Abstract
Many important real-world problems, such as patrol or search and rescue, could benefit from the ability to train teams of robots to coordinate. One major challenge to achieving such coordination is determining the best way for robots on such teams to communicate with each other. Typical approaches employ hand-designed communication schemes that often require significant effort to engineer. In contrast, this paper presents a new communication scheme called the hive brain, in which the neural network controller of each robot is directly connected to internal nodes of other robots and the weights of these connections are evolved. In this way, the robots can evolve their own internal “language” to speak directly brain-to-brain. This approach is tested in a multirobot patrol synchronization domain where it produces robot controllers that synchronize through communication alone in both simulation and real robots, and that are robust to perturbation and changes in team size.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)
Jackson, D.E., Ratnieks, F.L.: Communication in ants. Current Biology 16(15), R570–R574 (2006)
Riley, J., Greggers, U., Smith, A., Reynolds, D., Menzel, R.: The flight paths of honeybees recruited by the waggle dance. Nature 435(7039), 205–207 (2005)
D’Ambrosio, D.B., Lehman, J., Risi, S., Stanley, K.O.: Evolving policy geometry for scalable multiagent learning. In: Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 731–738. International Foundation for Autonomous Agents and Multiagent System (2010)
Bennett, M., Schatz, M.F., Rockwood, H., Wiesenfeld, K.: Huygens’s clocks. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 458(2019), 563–579 (2002)
Bowling, M., Veloso, M.: Multiagent learning using a variable learning rate. Artificial Intelligence 136(2), 215–250 (2002)
Hu, J., Wellman, M.P.: Multiagent reinforcement learning: theoretical framework and an algorithm. In: Proc. 15th International Conf. on Machine Learning, pp. 242–250. Morgan Kaufmann, San Francisco (1998)
Santana, H., Ramalho, G., Corruble, V., Ratitch, B.: Multi-agent patrolling with reinforcement learning. In: International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1122–1129 (2004)
Busoniu, L., Schutter, B.D., Babuska, R.: Learning and coordination in dynamic multiagent systems. Technical Report 05-019, Delft University of Technology (2005)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 3(11), 383–434 (2005)
Ficici, S., Pollack, J.: A Game-Theoretic Approach to the Simple Coevolutionary Algorithm. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN VI. LNCS, vol. 1917, pp. 467–476. Springer, Heidelberg (2000)
Panait, L., Wiegand, R., Luke, S.: Improving coevolutionary search for optimal multiagent behaviors. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 653–658 (2003)
Ren, W., Beard, R., Atkins, E.: A survey of consensus problems in multi-agent coordination. In: Proceedings of the 2005 American Control Conference, vol. 3, pp. 1859–1864 (June 2005)
Fax, J.A., Murray, R.M.: Information flow and cooperative control of vehicle formations. IEEE Transactions on Automatic Control 49(9), 1465–1476 (2004)
Sepulchre, R., Leonard, N.: Collective motion and oscillator synchronization. Electrical Engineering 309, 189–205 (2004)
Rodriguez-Angeles, A., Nijmeijer, H.: Mutual synchronization of robots via estimated state feedback: A cooperative approach. IEEE Trans. on Control Systems Technology 12(4), 542–554 (2004)
Yong, C.H., Miikkulainen, R.: Coevolution of role-based cooperation in multi-agent systems. IEEE Transactions on Autonomous Mental Development 1, 170–186 (2010)
Di Paolo, E.A.: Behavioral coordination, structural congruence and entrainment in a simulation of acoustically coupled agents. Adaptive Behavior 8(1), 27–48 (2000)
Floreano, D., Mitri, S., Magnenat, S., Keller, L.: Evolutionary conditions for the emergence of communication in robots. Current Biology 17(6), 514–519 (2007)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10, 99–127 (2002)
Bongard, J.C., Pfeifer, R.: Morpho-functional Machines: The New Species (Designing Embodied Intelligence). In: Evolving Complete Agents using Artificial Ontogeny, pp. 237–258. Springer (2003)
Hornby, G.S., Pollack, J.B.: Creating high-level components with a generative representation for body-brain evolution. Artificial Life 8(3), 223–246 (2002)
Stanley, K.O., Miikkulainen, R.: A taxonomy for artificial embryogeny. Artificial Life 9(2), 93–130 (2003)
Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based indirect encoding for evolving large-scale neural networks. Artificial Life 15(2) (2009)
Gauci, J., Stanley, K.O.: Autonomous evolution of topographic regularities in artificial neural networks. Neural Computation, 38 (2010) (to appear)
Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research 21(1), 63–100 (2004)
Stanley, K.O.: Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines 8(2), 131–162 (2007)
Udin, S., Fawcett, J.: Formation of topographic maps. Annual Review of Neuroscience 11(1), 289–327 (1988)
Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160, 106–154 (1962)
D’Ambrosio, D.B., Stanley, K.O.: Generative encoding for multiagent learning. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press, New York (2008)
D’Ambrosio, D.B., Lehman, J., Risi, S., Stanley, K.O.: Task switching in multiagent learning through indirect encoding. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, Piscataway (2011)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Green, C.: SharpNEAT homepage (2003-2006), http://sharpneat.sourceforge.net/
Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification 21, 63–100 (2004)
Ren, W., Beard, R., Atkins, E.: A survey of consensus problems in multi-agent coordination. In: Proceedings of the 2005 American Control Conference, pp. 1859–1864. IEEE (2005)
Verbancsics, P., Stanley, K.: Constraining connectivity to encourage modularity in HyperNEAT. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1483–1490. ACM (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
D’Ambrosio, D.B., Goodell, S., Lehman, J., Risi, S., Stanley, K.O. (2012). Multirobot Behavior Synchronization through Direct Neural Network Communication. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-33515-0_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33514-3
Online ISBN: 978-3-642-33515-0
eBook Packages: Computer ScienceComputer Science (R0)