A circuit complexity formulation of algorithmic information theory

Inspired by Solomonoff's theory of inductive inference (Solomonoff, 1964), we propose a prior based on circuit complexity. There are several advantages to this approach. First, it relies on a complexity measure that does not depend on the choice of Universal Turing machine (UTM). There is one universal definition for Boolean circuits involving a universal operation (e.g. NAND) with simple conversions to alternative definitions (with AND, OR, and NOT). Second, there is no analogue of the halting problem. The output value of a circuit can be calculated recursively by computer in time proportional to the number of gates, while a short program may run for a very long time. Our prior assumes that a Boolean function (or equivalently, Boolean string of fixed length) is generated by some Bayesian mixture of circuits. This model is appropriate for learning Boolean functions from partial information, a problem often encountered within machine learning as “binary classification.” We argue that an inductive bias towards simple explanations as measured by circuit complexity is appropriate for this problem.