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Asymptotically Optimal Agents

Lattimore, Tor; Hutter, Marcus


Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
Source: Lecture Notes in Artificial Intelligence 6925
DOI: 10.1007/978-3-642-24412-4_29
Access Rights: Open Access


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