Inductive Logic Programming (ILP) is a form of symbolic machine learning, where logic programs (e.g., Prolog or ASP) are synthesized from input/output examples. These synthesis tasks are notorious for their large search space. Modern approaches use a conflict-driven search to constrain this search iteratively. This procedure is usually implemented using constraint programming solvers operating on grounded logic programs. The ILP-systems such as Popper begin to stall when hypotheses are required featuring clauses with long bodies or multiple variables. This thesis topic, explores the use of alternative constraint programming solver not operating on the ground-then-solve paradigm such as s(CASP) [3], in the context of a conflict-driven ILP system.
Dodging the Grounding Bottleneck in Program Synthesis
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