The power of parallelism for automatic program synthesis

Abstract

Inductive inference machines (IIMs) are algorithmic devices which accept as input the graph of a computable function, an ordered pair at a time, and which output a succession of programs each conjectured to compute the input function. IIMs synthesize programs given examples of their intended input-output behavior. Several different criterion for successful synthesis by IIMs are defined. A given criterion is said to be more general than some other criterion if the class of sets which can be inferred by some IIM with respect to the given criteria is larger than the class of sets which can be inferred by some IIM with respect to the other criterion. The tradeoffs between the number of IIMs involved in the learning process and the generality of the criteria of success are examined.