Discovery of probabilistic rules for prediction.

An inductive learning algorithm is presented for analyzing the inherent patterns in the sequence and for predicting future objects based on these patterns. This inductive learning algorithm is divided into three phases: (1) detection of underlying patterns in a sequence of objects; (2) construction of rules, based on the detected patterns, that describe the generation process of the sequence; and (3) use of these rules to predict the characteristics of the future objects. The learning algorithm has been implemented in a program known as the OBSERVER, and it has been tested with both simulated and real-life data. The experimental results show that the OBSERVER is capable of discovering hidden patterns and explaining the behavior of certain sequence-generating processes that a user is not immediately aware of or fully understood. For this reason, the OBSERVER can be used to solve complex real-world problems where predictions have to be made in the presence of uncertainty.