The task of program synthesis, or automatically generating programs that are consistent with a provided specification, remains a challenging task in artificial intelligence. As in other fields of AI, deep learning-based end-to-end approaches have made great advances in program synthesis. However, more so than other fields such as computer vision, program synthesis provides greater opportunities to explicitly exploit structured information such as execution traces, which contain a superset of the information input/output pairs. While they are highly useful for program synthesis, as execution traces are more difficult to obtain than input/output pairs, we use the insight that we can split the process into two parts: infer the trace from the input/output example, then infer the program from the trace. This simple modification leads to state-of-the-art results in program synthesis in the Karel domain, improving accuracy to 81.3% from the 77.12% of prior work.

Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural models are effective at guiding program synthesis searches.

Neural Information Processing Systems http://nips.cc/

We take integrating with a symbolic system further: we use its internal representation as input to the neural model.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/