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On the computability of solomonoff induction and knowledge-seeking

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Date

2015

Authors

Leike, Jan
Hutter, Marcus

Journal Title

Journal ISSN

Volume Title

Publisher

Springer International Publishing Switzerland

Abstract

Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff’s prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.

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Citation

URI

http://hdl.handle.net/1885/103397

Collections

ANU Research Publications

Source

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

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DOI

10.1007/978-3-319-24486-0_24

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