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We introduce Knowledge-Driven Program Synthesis (KDPS) as a variant of theprogram synthesis task that requires the agent to solve a sequence of programsynthesis problems. In KDPS, the agent should use knowledge from the earlierproblems to solve the later ones. We propose a novel method based on PushGP tosolve the KDPS problem, which takes subprograms as knowledge. The proposedmethod extracts subprograms from the solution of previously solved problems bythe Even Partitioning (EP) method and uses these subprograms to solve theupcoming programming task using Adaptive Replacement Mutation (ARM). We callthis method PushGP+EP+ARM. With PushGP+EP+ARM, no human effort is required inthe knowledge extraction and utilization processes. We compare the proposedmethod with PushGP, as well as a method using subprograms manually extracted bya human. Our PushGP+EP+ARM achieves better train error, success count, andfaster convergence than PushGP. Additionally, we demonstrate the superiority ofPushGP+EP+ARM when consecutively solving a sequence of six program synthesisproblems.

Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives