Evolved Sensor Fusion and Dissociation in an Embodied Agent
Proceedings of the EPSRC/BBSRC International Workshop Biologically-Inspired Robotics: The Legacy of W. Grey Walter, , 102-109, 2002
Abstract: W. Grey Walter first demonstrated that an autonomous robot could follow an environmental gradient to its source. In this paper, neural networks are evolved that allow a simulated, embodied quadrupedal agent to sense and follow an environmental gradient - in this case, local chemical concentration - to its source. Through a series of ablation experiments performed in silico, it is shown how artificial evolution gradually integrates and dissociates the different sensor modalities available to the agent in order to produce chemotacting behaviour. This work builds on that of Walter by indicating that evolutionary methods automatically generate chemotaxis by modulating simpler behaviours (here, forward locomotion) using a sensor modality (chemosensors) separate from those driving the simpler behaviour. This suggests that evolutionary methods are well suited for automatically generating behaviours more complex than chemotaxis by using it in turn as a base behaviour.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).