Part of the Advanced Information and Knowledge Processing book series (AI&KP)
Self-Organization as Phase Transition in Decentralized Groups of Robots: A Study Based on Boltzmann Entropy
KeywordsPhase Transition Robotic System Autonomous Robot Entropy Index Neural Controller
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- Anderson, P. (1997). Basic Notions of Condensed Matter Physics. Perseus, Cambridge, MA.Google Scholar
- Beckers, R., Holland, O. E., and Deneubourg, J.-L. (1994). From local actions to global tasks: Stigmergy and collective robotics. In Brooks, R. A., and Maes, P., editors, Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems (Artificial Life IV), pages 181–189. MIT Press, Cambridge, MA.Google Scholar
- Baldassarre, G., Nolfi, S., and Parisi D. (2003). Evolution of collective behaviour in a group of physically linked robots. In Raidl, G., Guillot, A., and Meyer, J.-A., editors, Applications of Evolutionary Computing - Proceedings of the Second European Workshop on Evolutionary Robotics, pages 581-592. Springer, Berlin.Google Scholar
- Baldassarre, G., Parisi, D., and Nolfi, S. (2007a). Measuring coordination as entropy decrease in groups of linked simulated robots. In Minai, A., and Bar-Yam, Y. editors, Proceedings of the Fifth International Conference on Complex Systems (ICCS2004). Springer, Berlin, in press.Google Scholar
- Feldman, P. D. (1998). A brief introduction to information theory, excess entropy and computational mechanics. Technical report. Department of Physics, University of California.Google Scholar
- Nolfi, S., and Floreano, D. (2001). Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge, MA.Google Scholar
- Prokopenko, M., Boschetti, F., and Ryan, A. J. (2007). An information-theoretic primer on complexity, self-organisation and emergence. Advances in Complex Systems, submitted.Google Scholar
- Prokopenko, M., Gerasimov, V., and Tanev, I. (2006). Evolving spatiotemporal coordination in a modular robotic system. In Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D., editors, From Animals to Animats 9: Proceedings of the Ninth International Conference on the Simulation of Adaptive Behavior (SAB-2006), volume 4095 of Lecture Notes in Computer Science, pages 558–569. Springer, Berlin.Google Scholar
- Quinn, M., Smith, L., Mayley, G., and 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.MathSciNetCrossRefGoogle Scholar
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