On the Computability of Solomonoff Induction and Knowledge-Seeking
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.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2015
- arXiv:
- arXiv:1507.04124
- Bibcode:
- 2015arXiv150704124L
- Keywords:
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- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning
- E-Print:
- ALT 2015