A Method for Finding Consistent Hypotheses Using Abstraction

Authors:
Yoshiaki Okubo;Makoto Haraguchi;Yan Fang Zheng
Affiliations:
-;-;-
Venue:
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Year:
2000

Citing 5
Cited 0

Abstraction in planning

Reasoning about plans
A theory of abstraction

Artificial Intelligence
Automatically generating abstractions for planning

Artificial Intelligence
A goal-dependent abstraction for legal reasoning by analogy

Artificial Intelligence and Law
Constructing predicate mappings for goal-dependent abstraction

Annals of Mathematics and Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present in this paper a method for finding target hypotheses in Inductive Logic Programming (ILP). In order to find them efficiently, we propose to use abstraction. Given an ILP problem and a hypothesis space H, we first consider an abstraction of H. An abstract space corresponds to a small subspace of H. Then we try to find hypotheses satisfying a certain condition by searching in several such abstract spaces. Since each abstract space is small, the task is not difficult. From these hypotheses, we can easily identify a hypothesis space in which all consistent hypotheses can be found. Since the obtained space is a part of the original H, we can expect that the targets are efficiently found by searching only in the part.