Abstract: Interactive Evolutionary Algorithms (IEAs) use human input to help drive a search process. Traditionally, IEAs allow the user to exhibit preferences among some set of individuals. Here we present a system in which the user directly demonstrates what he or she prefers. Demonstration has an advantage over preferences because the user can provide the system with a solution that would never have been presented to a user who can only provide preferences. However, demonstration exacerbates the user fatigue problem because it is more taxing than exhibiting preferences. The system compensates for this by retaining and reusing the user demonstration, similar in spirit to user modeling. The system is exercised on a robot locomotion and obstacle avoidance task that has an obvious local optimum. The user demonstration is provided through low-level control. The system is compared against a high-level fitness function that is susceptible to becoming trapped by a local optimum and a mid-level fitness function designed to remove the local optimum. We show that our proposed system outperforms most variants of these completely automatic methods, providing further evidence that Evolutionary Robotics (ER) can benefit by combining the intuitions of inexpert human users with the search capabilities of computers.
**May not be in order
[edit database entry]
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).