Automatic synthesis of multiple internal models through active exploration
AAAI Fall Symposium: From Reactive to Anticipatory Cognitive Embodied Systems, , , 2005
Abstract: An important question in cognitive science is whether internal models are encoded in the brain of higher animals at birth, and are only subsequently refined through experience, or whether models are synthesized over the lifetime of an animal–and if so, how are they formed. A further question is whether animals maintain a single model of a particular body part or tool, or whether multiple competing models are maintained simultaneously. In this paper we describe a co-evolutionary algorithm that automatically synthesizes and maintains multiple candidate models of a behaving robot. These predictive models can then be used to generate new controllers to either elicit some desired behavior under uncertainty (where competing models agree on the resulting behavior); or determine actions that uncover hidden components of the target robot (where models disagree, indicating further model synthesis is required). We demonstrate automated model synthesis from sensor data; model synthesis ‘from scratch’(little initial knowledge about the robot’s morphology is assumed); and integrated, continued model synthesis and controller design. This new modeling methodology may shed light on how models are acquired and maintained in higher organisms for the purpose of prediction and anticipation.
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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).