Universal Sequential Decisions in Unknown Environments

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dc.creatorHutter, Marcus
dc.date2003-06-16
dc.date2004-09-30
dc.date.accessioned2026-07-07T03:19:54Z
dc.date.available2026-07-07T03:19:54Z
dc.descriptionWe give a brief introduction to the AIXI model, which unifies and overcomes the limitations of sequential decision theory and universal Solomonoff induction. While the former theory is suited for active agents in known environments, the latter is suited for passive prediction of unknown environments.
dc.description2 pages
dc.identifierhttps://arxiv.org/abs/cs/0306091
dc.identifierhttp://arxiv.org/abs/cs/0306091
dc.identifierProc. 5th European Workshop on Reinforcement Learning (EWRL-2001) 25-26
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31649
dc.subjectArtificial Intelligence
dc.subjectComputational Complexity
dc.subjectMachine Learning
dc.subjectI.2; G.3
dc.titleUniversal Sequential Decisions in Unknown Environments
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