ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctions

Logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge in logic programming. In this paper I consider the problem of learning ground LPADs starting from a set of interpretations annotated with their probability. I present the system ALLPAD for solving this problem. ALLPAD modifies the previous system LLPAD in order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the problem of classifying proteins according to their tertiary structures and the results compare favorably with most other approaches.