Abstract: Recently it has been demonstrated that collaboration between
automated algorithms and human users can be especially effective
in robot behavior optimization tasks. In particular, we
recently introduced a Fitness-based Search with Preferencebased
Policy Learning (FS-PPL) approach, in which the algorithm
models the user based on her preferences and then uses
the model, along with the fitness function, to guide search.
However, so far only interaction between a single human user
and an evolutionary algorithm was considered. If multiple
users contribute preferences, the algorithm must determine
whether to model them separately or jointly. In this paper we
describe an algorithm in which one evolutionary algorithm interacts
with two users and determines the best way to model
them automatically. We test the algorithm with automated
substitutes for human users and show that it performs better
for two users working together than for the same users working
separately, thus demonstrating the potential for crowdsourcing
robot behavior optimization.
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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).