AJ
A. Jelev
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Program synthesis is a computationally difficult task. Budgeted search seems to be a relatively unexplored method of performing program synthesis tasks. This paper describes a budgeted program synthesis implementation that makes use of subprograms it discovers during attempts and leverages them in future attempts in hopes of finding an optimal solution faster. The approach performed slightly worse than most of the control synthesis task runs during experiments. However, some specific cases were discovered for which the budgeted synthesis ran faster than the control synthesis task.
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Program synthesis is a computationally difficult task. Budgeted search seems to be a relatively unexplored method of performing program synthesis tasks. This paper describes a budgeted program synthesis implementation that makes use of subprograms it discovers during attempts and leverages them in future attempts in hopes of finding an optimal solution faster. The approach performed slightly worse than most of the control synthesis task runs during experiments. However, some specific cases were discovered for which the budgeted synthesis ran faster than the control synthesis task.