Emergence of Glider-like Structures in a Modular Robotic System

Information-driven evolutionary design has been proposed as an efficient method for designing self-organized multi-agent systems. Information transfer is known to be an important component of distributed computation in many complex systems, and indeed it has been suggested that maximization of information transfer can give rise to interesting behavior and induce necessary structure in a system. In this paper, we report the first known application of a direct measure of information transfer, transfer entropy, as a fitness function to evolve a self-organized multi-agent system. The system evolved here is a simulated snake-like modular robot. In the most fit snakebot in the final generation, we observe coherent traveling information transfer structures. These are analogous to gliders in cellular automata, which have been demonstrated to represent the coherent transfer of information across space and time, and play an important role in facilitating distributed computation. These observations provide evidence that using information transfer to drive evolutionary design can produce useful structure in the underlying system.

[1]  J. Tuszynski,et al.  A review of the ferroelectric model of microtubules , 1999 .

[2]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[3]  Melanie Mitchell,et al.  Evolving cellular automata to perform computations: mechanisms and impediments , 1994 .

[4]  Olaf Sporns,et al.  Mapping Information Flow in Sensorimotor Networks , 2006, PLoS Comput. Biol..

[5]  N. Ay,et al.  Information and closure in systems theory , 2006 .

[6]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[7]  Ursula Kummer,et al.  Information transfer in signaling pathways: A study using coupled simulated and experimental data , 2008, BMC Bioinformatics.

[8]  J. Crutchfield,et al.  Regularities unseen, randomness observed: levels of entropy convergence. , 2001, Chaos.

[9]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[10]  Olaf Sporns,et al.  Evolving Coordinated Behavior by Maximizing Information Structure , 2006 .

[11]  Daniel Polani,et al.  How Information and Embodiment Shape Intelligent Information Processing , 2006, 50 Years of Artificial Intelligence.

[12]  Carl P. Dettmann,et al.  Microscopic Chaos and Diffusion , 2000, nlin/0001062.

[13]  S. Pincus,et al.  Randomness and degrees of irregularity. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Ricard V. Solé,et al.  Phase Transitions in a Model of Internet Traffic , 2000 .

[15]  Albert Y. Zomaya,et al.  Detecting Non-trivial Computation in Complex Dynamics , 2007, ECAL.

[16]  Albert Y. Zomaya,et al.  Local information transfer as a spatiotemporal filter for complex systems. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  C. Adami What is complexity? , 2002, BioEssays : news and reviews in molecular, cellular and developmental biology.

[18]  Chrystopher L. Nehaniv,et al.  All Else Being Equal Be Empowered , 2005, ECAL.

[19]  Stefano Nolfi,et al.  Evolving coordinated group behaviours through maximisation of mean mutual information , 2008, Swarm Intelligence.

[20]  M. Prokopenko,et al.  Evolving Spatiotemporal Coordination in a Modular Robotic System , 2006, SAB.

[21]  Mikhail Prokopenko,et al.  Evolving Spatiotemporal Coordination in a Modular Robotic System , 2006, SAB.

[22]  D. Parisi,et al.  Measuring Coordination as Entropy Decrease in Groups of Linked Simulated Robots , 2005 .

[23]  Albert Y. Zomaya,et al.  A framework for the local information dynamics of distributed computation in complex systems , 2008, ArXiv.

[24]  Ivan Tanev,et al.  Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot , 2005, IEEE Transactions on Robotics.

[25]  Olaf Sporns,et al.  Evolution of Neural Structure and Complexity in a Computational Ecology , 2006 .

[26]  Albert Y. Zomaya,et al.  The Information Dynamics of Phase Transitions in Random Boolean Networks , 2008, ALIFE.