Learning relational rule from examples that are neither positive nor negative.

Over the past decade, several inductive logic programming (ILP) systems have been developed. However, the normal ILP system does not have enough power to induce logic programs in some domains. Therefore, various new ILP systems, such as nonmonotonic ILP, that apply new learning tasks, have been proposed. In the present paper, a new learning task, called relational learning from nondichotomizable examples, is proposed for relational domains. The scheme of this task is similar to that of normal ILP, with the exception of the training examples. Normally, conventional ILP uses positive and negative examples in the training process. However, the examples of the proposed learning task are not strictly positive or negative: the training examples have a continuous scale. This new learning task is defined and a new method for learning this task is proposed. A new ILP system, SYNGIP (for SYNthesized system using Genetic programming and Inductive logic Programming), is developed based on this method and the performance of SYNGIP is compared experimentally to that of the conventional ILP method for the task of human feeling acquisition. © 2001 Scripta Technica, Syst Comp Jpn, 32(14): 34–40, 2001 [ABSTRACT FROM AUTHOR]