Anytime Inductive Logic Programming

Anytime algorithms refers to algorithms that "always can produce a result. Often the result of the algorithm depends on the time at hand, the longer the time, the better the answer. In this paper we present an easy way of turning regular Inductive Logic Programming (ILP) algorithms such as Divide-And-Conquer (DAC.) and Separate-And-Conquer (SAC) into anytime algorithms. We conduct experiments with these anytime algorithms and introduce a simple heuristic called squared quota, that we compare with an established one, information gain. It seems that squared quota is better suited for a small window size of example data, and hence better to use in anytime systems. A comparison between SAC and DAC reveals that they excel in different combinations of examples/background knowledge.