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Abstract

The ability to form abstractions and to generalize well from few samples are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such flexibility in artificial systems is challenging, particularly because the underlying computational principles are not fully understood. This talk introduces an information-theoretic framework for bounded rational decision-making, that is optimal decision-making under limited computational resources. One consequence of acting optimally under computational limitations is the emergence of natural abstractions which allow for more efficient processing of information. The consequent application of the theoretical framework to perception-action systems results in an interesting optimality principle that leads to a tight coupling between perception and action. As a result, the objective of bounded-optimal perception is not to represent a sensory state as faithfully as possible, but rather to extract the most relevant information for bounded-optimal acting.