MPL-Core: An Efficient Multiple Predicate Learner Based on Fast Failure Mechanism

概要

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Existing inductive logic programming (ILP) systems learn only one target predicate from a set of background predicates and a set of positive and negative examples. Therefore, such ILP systems cannot deal with problems which involve several target predicates. This paper presents a multiple predicate learning (MPL) system with a new learning approach. MPL systems are usually prevented from learning target predicates when the constants appearing in the examples of one target predicate differ from those of the others, e.g., the Dutch flag problem is problematic for existing MPL systems. Our system, MPL-Core, has ability to tackle the case and efficiently learns from multiple predicate tasks. Core, a single predicate learning module, has a fast failure mechanism and can select refinement operators based on the learning task. By means of GPC, an efficient pruning method, Core effectively prunes unpromising branches in a search tree, making the search space more tractable. Furthermore, our algorithm uses the fast failure mechanism which gives it a distinct advantage over existing multiple predicate learning algorithms in terms of computational complexity. Experimental results show the learning efficiency and potential learning ability of MPL-Core. The effect of the fast failure mechanism is also shown by experimental comparisons on learning from both artificial and practical domains.
社団法人人工知能学会の論文
1997-07-01