Abstract: THE AUTOMATED DESIGN, construction, and deployment
of autonomous and adaptive machines is an open
problem. Industrial robots are an example of
autonomous yet nonadaptive machines: they execute
the same sequence of actions repeatedly. Conversely,
unmanned drones are an example of adaptive yet
non-autonomous machines: they exhibit the adaptive
capabilities of their remote human operators. To date,
the only force known to be capable of producing fully
autonomous as well as adaptive machines is biological
evolution. In the field of evolutionary robotics,9
class of population-based metaheuristics—evolutionary
algorithms—are used to optimize some or all aspects of
an autonomous robot. The use of metaheuristics sets
this subfield of robotics apart from the mainstream
of robotics research, in which machine learning
algorithms are used to optimize the control policya
robot. As in other branches of computer science the use
of a metaheuristic algorithm has a cost and a benefit.
The cost is that it is not possible to guarantee if (or
when) an optimal control policy will be found for a given
robot. The benefit is few assumptions must be made...
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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).