Evolving coordinated group behaviours through maximisation of mean mutual information
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Baldassarre, G., Trianni, V., Bonani, M., Mondada, F., Dorigo, M., & Nolfi, S. (2007). Self-organised coordinated motion in groups of physically connected robots. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 37(1), 224–239.
Brenner, N., Bialek, W., & de Ruyter van Steveninck, R. (2000). Adaptive rescaling maximizes information transmission. Neuron, 26, 695–702.
Capdepuy, P., Polani, D., & Nehaniv, C. (2007). Maximization of potential information flow as a universal utility for collective behaviour. In Proceedings of the 2007 IEEE symposium on artificial life (CI-ALife 2007) (pp. 207–213). Piscataway: IEEE Press.
Cianci, C. M., Raemy, C., Pugh, J., & Martinoli, A. (2007). Communication in a swarm of miniature robots: the e-puck as an educational tool for swarm robotics. In E. Şahin, W. M. Spears, & A. F. T. Winfield (Eds.), Lecture notes in computer science: Vol. 4433. Swarm robotics—second SAB 2006 international workshop, revised selected papers (pp. 103–115), Rome, Italy, September 30–October 1, 2006. Berlin: Springer.
Feldman, D. (2002). A brief introduction to: information theory, excess entropy and computational mechanics. (Technical report). College of the Atlantic, Bar Harbor, ME.
Funes, P., Orme, B., & Bonabeau, E. (2003). Evolving emergent group behaviors for simple humans agents. In W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, & J. Ziegler (Eds.), Lecture notes in artificial intelligence: Vol. 2801. Advances in artificial life. Proceedings of the 7th European conference on artificial life (ECAL 2003) (pp. 76–89). Berlin: Springer.
Jakobi, N. (1997). Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior, 6, 325–368.
Klyubin, A., Polani, D., & Nehaniv, C. (2005a). All else being equal being empowered. In M. Capcarrere, A. A. Freitas, P. J. Bentley, C. G. Johnson, & J. Timmis (Eds.), Lecture notes in artificial intelligence: Vol. 3630. Advances in artificial life. Proceedings of the 8th European conference on artificial life (ECAL 2005) (pp. 744–753). Berlin: Springer.
Klyubin, A., Polani, D., & Nehaniv, C. (2005b). Empowerment: a universal agent-centric measure of control. In Proceedings of the 2005 IEEE congress on evolutionary computation (pp. 128–135). Piscataway: IEEE Press.
Lungarella, M., & Pfeifer, R. (2001). Robots as cognitive tools: Information theoretic analysis of sensory-motor data. In Proceedings of the 2nd international IEEE/RSJ conference on humanoid robotics (pp. 245–252). Piscataway: IEEE Press.
Lungarella, M., & Sporns, O. (2005). Information self-structuring: key principle for learning and development. In Proceedings of the 4th international conference on development and learning (pp. 25–30). Piscataway: IEEE Press.
Lungarella, M., Pegors, T., Bulwinkle, D., & Sporns, O. (2005). Methods for quantifying the information structure of sensory and motor data. Neuroinformatics, 3(3), 243–262.
Miglino, O., Lund, H., & Nolfi, S. (1995). Evolving mobile robots in simulated and real environments. Artificial Life, 2(4), 417–434.
Mondada, F., & Bonani, M. (2007). The e-puck education robot. http://www.e-puck.org/ .
Nolfi, S., & Floreano, D. (2000). Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. Cambridge: MIT Press/Bradford Books.
Olsson, L., Nehaniv, C., & Polani, D. (2005). Sensor adaptation and development in robots by entropy maximization of sensory data. In Proceedings of the 6th IEEE international symposium on computational intelligence in robotics and automation (CIRA-2005) (pp. 587–592). Piscataway: IEEE Computer Society.
Prokopenko, M., & Wang, P. (2003). Evaluating team performance at the edge of chaos. In D. Polani, B. Browning, A. Bonarini, & K. Yoshida (Eds.), Lecture notes in computer science: Vol. 3020. RoboCup 2003: robot soccer world cup VII (pp. 89–101). Berlin: Springer.
Prokopenko, M., Gerasimov, V., & Tanev, I. (2006a). Evolving spatiotemporal coordination in a modular robotic system. In S. Nolfi, G. Baldassarre, R. Calabretta, J. C. T. Hallam, D. Marocco, J. A. Meyer, O. Miglino, & D. Parisi (Eds.), From animals to animats 9: 9th international conference on the simulation of adaptive behavior (SAB 2006) (pp. 558–569). Berlin: Springer.
Prokopenko, M., Gerasimov, V., & Tanev, I. (2006b). Measuring spatiotemporal coordination in a modular robotic system. In L. M. Rocha, L. S. Yaeger, M. A. Bedau, D. Floreano, R. L. Goldstone, & A. Vespignani (Eds.), Artificial life X: proceedings of the tenth international conference on the simulation and synthesis of living systems (pp. 185–191). Cambridge: MIT Press.
Quinn, M., Smith, L., Mayley, G., & Husbands, P. (2003). Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences, 361, 2321–2344.
Shannon, C. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423 and 623–656.
Sporns, O., & Lungarella, M. (2006). Evolving coordinated behavior by maximizing information structure. In L. M. Rocha, L. S. Yaeger, M. A. Bedau, D. Floreano, R. L. Goldstone, & A. Vespignani (Eds.), Artificial life X: proceedings of the tenth international conference on the simulation and synthesis of living systems (pp. 323–329). Cambridge: MIT Press.
Sporns, O., Tononi, G., & Edelman, G. (2000). Connectivity and complexity, the relationship between neuroanatomy and brain dynamics. Neural Networks, 13, 909–922.
Tarapore, D., Lungarella, M., & Gomez, G. (2004). Fingerprinting agent-environment interaction via information theory. In F. Groen, N. Amato, A. Bonarini, E. Yoshida, & B. Kröse (Eds.), Intelligent autonomous systems 8 (pp. 512–520). Amsterdam: IOS.
Tarapore, D., Lungarella, M., & Gomez, G. (2006). Quantifying patterns of agent-environment interaction. Robotics and Autonomous Systems, 54(2), 150–158.
Tononi, G., Sporns, O., & Edelman, G. (1994). A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences, 91, 5033–5037.
Tononi, G., Sporns, O., & Edelman, G. G. (1996). A complexity measure for selective matching of signals by the brain. Proceedings of the National Academy of Sciences, 93, 3422–3427.
Tononi, G., Edelman, G., & Sporns, O. (1998). Complexity and coherency: integrating information in the brain. Trends in Cognitive Sciences, 2(12), 474–484.
Trianni, V., & Nolfi, S. (2007). Minimal communication strategies for self-organising synchronisation behaviours. In Proceedings of the 2007 IEEE symposium on artificial life (CI-ALife 2007) (pp. 199–206). Piscataway: IEEE Press.
Trianni, V., Nolfi, S., & Dorigo, M. (2008). Evolution, self-organisation and swarm robotics. In C. Blum & D. Merkle (Eds.), Natural computing series. Swarm intelligence. Introduction and applications. Berlin: Springer.