User:Felix QW/Inductive logic programming

Approaches to ILP

Inductive logic programming systems can be roughly divided into two classes, search-based and meta-interpretative systems.

Search-based systems exploit that the space of possible clauses forms a complete lattice under the subsumption relation, where one clause ${\textstyle C_{1}}$ is a refinement of another clause ${\textstyle C_{2}}$ if there is a substitution ${\textstyle \theta }$ such that ${\textstyle C_{1}\theta }$, the result of applying ${\textstyle \theta }$ to ${\textstyle C_{1}}$, is a subset of ${\textstyle C_{2}}$. This lattice can be traversed either bottom-up or top-down.

Bottom-up search

• Sketches of Golem
• Inverse resolution
• Least general generalisations

Top-down search

• Sketch of Progol/Aleph/FOIL and the Model Inference System

Metainterpretative learning

• Metarules
• Constraint-based learning

Common issues

• Language bias
• Scoring functions
• Predicate invention
• Learning recursive programs

Applications

• Bioinformatics
• Other areas

References

 This article incorporates text from a free content work. Licensed under CC-BY 4.0 (license statement/permission). Text taken from A History of Probabilistic Inductive Logic Programming​, Fabrizio Riguzzi, Elena Bellodi and Riccardo Zese, Frontiers Media.