Abstract: In this report we introduce an artificial evolutionary system, Artificial Ontogeny (AO), that uses a developmental encoding scheme to translate a given genotype into a complete agent, which then acts in a physically-realistic virtual environment. Evolution is accomplished using a genetic algorithm, in which the genotypes are treated as genetic regulatory networks. The dynamics of the regulatory network direct the growth of the agent, and lead to the construction of both the morphology and neural control of the agent. We demonstrate that such a model can be used to evolve agents to perform non-trivial tasks, such as directed locomotion and block pushing in a noisy environment. It is shown that mutations expressed earlier in development tend to have a more variable morphological and behavioural effect than mutations expressed later in development, which tend to have a less pronounced effect. These results support the hypothesis that ontogeny provides artificial evolution with beneficial mutations that have varying degrees of phenotypic effect, depending on their onset of expression during development. In addition, we evolve agents using a fitness function which indirectly selects for increased size. In these agents we find evidence of functional specialization and repeated, differentiated structure. In the final section we argue that such a system would be a useful tool for the evolutionary design of morpo-functional machines.
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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).