Parallel Constraint-Driven Inductive Logic Programming
This work addresses a scalability problem for researchers and practitioners using ILP, but it is incremental as it applies parallelization to an existing constraint-driven approach.
The paper tackled the scalability limitation of inductive logic programming (ILP) on multi-core machines by introducing parallel techniques based on constraint-driven ILP, resulting in substantially reduced learning times as shown in experiments on program synthesis and inductive general game playing domains.
Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques based on constraint-driven ILP where the goal is to accumulate constraints to restrict the hypothesis space. Our experiments on two domains (program synthesis and inductive general game playing) show that (i) parallelisation can substantially reduce learning times, and (ii) worker communication (i.e. sharing constraints) is important for good performance.