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Tim Genewein

Information-optimal hierarchies for inference and decision-making

London, UK

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Genewein T (2016) Information-theoretic bounded rationality in perception-action systems. ICRA 2016 workshop on task-driven perceptual representations: sensing, planning and control under resource constraints, Stockholm.

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I was given the opportunity to present my work on information-theoretic bounded rationality in perception-action systems with a talk at the workshop on task-driven perceptual representations: sensing, planning and control under resource constraints at ICRA 2016 in Stockholm . In the talk I presented the information-theoretic framework for bounded rationality that trades off large expected utility against low information processing cost (see the Research pages for more information or alternatively this paper).

The central idea of the optimality principle is that any change in behavior incurs computation and in an agent with limited computational capacity this computation is costly. The cost of computation should thus be traded off against achieving a high expected utility. Interestingly, the resulting principle is identical to the rate distortion problem (the information-theoretic framework for lossy compression) and has strong formal ties to free energy minimization in thermodynamics. The problem in lossy compression is essentially the same as when forming abstractions: relevant information must be separated from irrelevant information (noise). The rate distortion principle for bounded rational decision-making has a parameter that governs the trade off between utility and information processing cost and one result (see here) is that changing this parameter leads to the emergence of natural levels of abstraction.

In the second part of the talk, the information-theoretic principle for bounded rationality is applied to a two-stage perception-action system. In short, the system has two computational stages that are subject to limited computational resources: a perceptual stage that takes a world-state and transforms it into an (internal) percept and an action-stage that computes optimal actions in response to the world-state, based only on the percept. Classically, perception is often treated as an inference problem and correspondingly the goal of perception is to represent the (latent) world-state as faithfully as possible, meaning that the world-state can be predicted well from the percept. Importantly, the inference-problem is decoupled from the action-part of the system.
The consequent application of the information-theoretic principle requires to trade off gains in utility against the cost that the computation on both stages (perception and action) incurs. Solutions to the resulting optimality principle for perception and action under limited computational resources lead to a tight coupling between perception and action, suggesting that the goal of bounded-optimal perception is to extract the most relevant information for acting which does not necessarily imply that the true world-state can be predicted well from the percept. For instance, consider a case where the world-state carries a lot of information that is completely irrelevant for acting: if the perceptual stage has limited computational resources, then its goal should be to extract the relevant information for acting rather than capturing a lot of the irrelevant information. This naturally couples perception and action. See more on this in our publication, including an intuitive, illustrative example.