Scott Garrabrant (Machine Intelligence Research Institute, Berkeley, CA) |
Tsvi Benson-Tilsen (Machine Intelligence Research Institute, Berkeley, CA) |
Andrew Critch (Machine Intelligence Research Institute, Berkeley, CA) |
Nate Soares (Machine Intelligence Research Institute, Berkeley, CA) |
Jessica Taylor (Machine Intelligence Research Institute, Berkeley, CA) |

We present the logical induction criterion for computable algorithms that assign probabilities to every logical statement in a given formal language, and refine those probabilities over time. The criterion is motivated by a series of stock trading analogies. Roughly speaking, each logical sentence phi is associated with a stock that is worth $1 per share if phi is true and nothing otherwise, and we interpret the belief-state of a logically uncertain reasoner as a set of market prices, where pt_N(phi)=50% means that on day N, shares of phi may be bought or sold from the reasoner for 50%. A market is then called a logical inductor if (very roughly) there is no polynomial-time computable trading strategy with finite risk tolerance that earns unbounded profits in that market over time. We then describe how this single criterion implies a number of desirable properties of bounded reasoners; for example, logical inductors outpace their underlying deductive process, perform universal empirical induction given enough time to think, and place strong trust in their own reasoning process. |

Published: 25th July 2017.

ArXived at: https://dx.doi.org/10.4204/EPTCS.251.16 | bibtex | |

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