  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.
