Inverse Entailment

An inverse entailment operator O(B,D) takes the training data D and background knowledge B as input and outputs a hypothesis $h$ such that \[ O(B,D) = h \text{ where } \forall_{\langle x_{i},f(x_{i})\rangle \in D } (B \wedge h \wedge x_{i}) \entails f(x_{i}) \]
Of course, there will usually be many $h$ that satisfy this.
A common heuristic is to use the Minimum Description Length principle.