Relevancy of background knowledge in inductive logic programming
AbstractInductive logic programming (ILP) is a form of machine learning which learns computer programs from input/output examples of a target program. To improve learning efficiency, ILP systems use background knowledge (i.e. auxiliary functions such as partition and append). However, most ILP systems cannot handle large amounts of background knowledge, and overcoming this limitation is a key challenge in ILP. The goal of this project is to explore techniques to identify relevant background knowledge. There is much freedom with this project, where one could focus on logical aspects, such as finding logically redundant background knowledge, or one could instead focus on statistical aspects, such as finding background knowledge most likely to be relevant for a given task. This work is a mix of theory, implementation, and experiments.
Prerequisites: familiarity with statistics, statistical machine learning, and ideally logic programming