Synchronizing to the Environment: Information Theoretic Constraints on Agent Learning

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dc.creatorCrutchfield, James P.
dc.creatorFeldman, David P.
dc.date2001-03-23
dc.date.accessioned2026-07-07T05:33:22Z
dc.date.available2026-07-07T05:33:22Z
dc.descriptionWe show that the way in which the Shannon entropy of sequences produced by an information source converges to the source's entropy rate can be used to monitor how an intelligent agent builds and effectively uses a predictive model of its environment. We introduce natural measures of the environment's apparent memory and the amounts of information that must be (i) extracted from observations for an agent to synchronize to the environment and (ii) stored by an agent for optimal prediction. If structural properties are ignored, the missed regularities are converted to apparent randomness. Conversely, using representations that assume too much memory results in false predictability.
dc.description6 pages, 5 figures, Santa Fe Institute Working Paper 01-03-020, http://www.santafe.edu/projects/CompMech/papers/stte.html
dc.identifierhttps://arxiv.org/abs/nlin/0103038
dc.identifierhttp://arxiv.org/abs/nlin/0103038
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/79971
dc.subjectAdaptation and Self-Organizing Systems
dc.titleSynchronizing to the Environment: Information Theoretic Constraints on Agent Learning
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