Robots can ground crowd-proposed symbols by forming theories of group mind
Proceedings of the 15th International Conference on the Synthesis and Simulation of Living Systems (ALife 2016), , , 2016
Abstract: The non-embodied approach to teaching machines language is to train them on large text corpora. However, this approach has yielded limited results. The embodied approach, in contrast, involves teaching machines to ground abstract symbols in their sensory-motor experiences, but how—or whether—humans achieve this remains largely unknown. We posit that one avenue for achieving this is to view language acquisition as a three-way interaction between linguistic, sensorimotor, and social dynamics: when an agent acts in response to a heard word, it is considered to have successfully grounded that symbol if it can predict how observers who understand that word will respond to the action. Here we introduce a methodology for testing this hypothesis: human observers issue arbitrary commands to simulated robots via the web, and provide positive or negative …
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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).