Inductive inference of recursive functions: complexity bounds

This survey includes principal results on complexity of inductive inference for recursively enumerable classes of total recursive functions. Inductive inference is a process to find an algorithm from sample computations. In the case when the given class of functions is recursively enumerable it is easy to define a natural complexity measure for the inductive inference, namely, the worst-case mindchange number for the first n functions in the given class. Surely, the complexity depends not only on the class, but also on the numbering, i.e. which function is the first, which one is the second, etc. It turns out that, if the result of inference is Goedel number, then complexity of inference may vary between log n+o(log2n ) and an arbitrarily slow recursive function. If the result of the inference is an index in the numbering of the recursively enumerable class, then the complexity may go up to const-n. Additionally, effects previously found in the Kolmogorov complexity theory are discovered in the complexity of inductive inference as well

Saying Hello World with MOLA - A Solution to the TTC 2011 Instructive Case

This paper describes the solution of Hello World transformations in MOLA transformation language. Transformations implementing the task are relatively straightforward and easily inferable from the task specification. The required additional steps related to model import and export are also described.Comment: In Proceedings TTC 2011, arXiv:1111.440