On the Computability of Solomonoff Induction and Knowledge-Seeking
Artificial Intelligence
2015-10-20 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1507.04124,
title = {On the Computability of Solomonoff Induction and Knowledge-Seeking},
author = {Jan Leike and Marcus Hutter},
journal= {arXiv preprint arXiv:1507.04124},
year = {2015}
}
Comments
ALT 2015