Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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A well-known problem in complex cognition is the so-called dynamic stocks and flows task (DSF). The challenge in this task is to control different flows, e.g. the inflows and outflows of water to a tank, towards a specified goal configuration, i.e. a certain amount of water in the tank. The problem is that some flows are exogenously controlled with a hidden dynamic. These flows need to be counterbalanced by setting endogenous flows. Since the dynamic underlying the hidden flows can be any computable function, this task can be classified as computationally complex. Psychological findings show that humans have difficulties in dealing with such dynamic systems. In this article, we present a formal generalization of this task and present a computational approach for solving such tasks as a first step towards an assistance system for complex system control.