Predicate Invention by Learning From Failures
  Discovering novel high-level concepts is one of the most important steps
needed for human-level AI. In inductive logic programming (ILP), discovering
novel high-level concepts is known as predicate invention (PI). Although seen
as crucial since the founding of ILP, PI is notoriously difficult and most ILP
systems do not support it. In this paper, we introduce POPPI, an ILP system
that formulates the PI problem as an answer set programming problem. Our
experiments show that (i) PI can drastically improve learning performance when
useful, (ii) PI is not too costly when unnecessary, and (iii) POPPI can
substantially outperform existing ILP systems.
