Viability of Inductive Logic Programming as a learning mechanism in real-time systems

Automatic learning systems now consider the time as the significatnt component and it has good impact and hence it should be considered while developing these systems. Many studies have addressed this issue and one amont them is the Allen’s temporal interval, based on a set of 13 relations that may hold between two time intervals. We in our study have kept the principal notion of identifying the several of temporal relations from data, using an inductive logic programming (ILP) system. This work addresses a series of automatic learning investigations. This study shows the following: The exploration of the impact of the negative training patterns on the induced relation, evidencing the necessary background knowledge for inducing the exact expression of the target concept and investigate the viability of ILP as a learning mechanism in real-time systems.