Explanation-Based Acceleration of Similarity-Based Learning

Program synthesis by examples is more convenient than using conventional techniques, since it only requires examples instead of a detailed specification. In spite of this convenience, relatively little research in this type of synthesis has so far been carried out. A major obstacle is that due to the similarity-based and data-driven features of this technique, many examples and considerable computation power are required to synthesize a complex program. To overcome this difficulty, we propose to accelerate the synthesis process based on explanations, by having the system explain how other similar programs satisfy given examples and then transfer the explanation to the target program. Since the programming know-how involved in the example programs is already available, new programs are easily synthesized using only a small number of examples.