  One of the most challenging goals in designing intelligent systems is
empowering them with the ability to synthesize programs from data. Namely,
given specific requirements in the form of input/output pairs, the goal is to
train a machine learning model to discover a program that satisfies those
requirements. A recent class of methods exploits combinatorial search
procedures and deep learning to learn compositional programs. However, they
usually generate only toy programs using a domain-specific language that does
not provide any high-level feature, such as function arguments, which reduces
their applicability in real-world settings. We extend upon a state of the art
model, AlphaNPI, by learning to generate functions that can accept arguments.
This improvement will enable us to move closer to real computer programs.
Moreover, we investigate employing an Approximate version of Monte Carlo Tree
Search (A-MCTS) to speed up convergence. We showcase the potential of our
approach by learning the Quicksort algorithm, showing how the ability to deal
with arguments is crucial for learning and generalization.
