Name
Abstract | ||
|---|---|---|
Program synthesis is the process of automatically translating a specification into computer code. Traditional synthesis settings require a formal, precise specification. Motivated by computer education applications where a student learns to code simple turtle-style drawing programs, we study a novel synthesis setting where only a noisy user-intention drawing is specified. This allows students to sketch their intended output, optionally together with their own incomplete program, to automatically produce a completed program. We formulate this synthesis problem as search in the space of programs, with the score of a state being the Hausdorff distance between the program output and the user drawing. We compare several search algorithms on a corpus consisting of real user drawings and the corresponding programs, and demonstrate that our algorithms can synthesize programs optimally satisfying the specification. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Visual specification,Search algorithm,Programming language,Program synthesis,Computer science,Source code,Computer education,Hausdorff distance,Artificial intelligence,Machine learning,Sketch |
DocType | Volume | Citations |
Journal | abs/1806.00938 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evan Hernandez | 1 | 0 | 0.68 |
| Ara Vartanian | 2 | 2 | 1.39 |
| Xiaojin Zhu | 3 | 3586 | 222.74 |