HETEROGENEOUS LEARNING IN THE DOPPELGANGER USER MODELING SYSTEM

DOPPELGANGER is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. DOPPELGANGER'S learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on DOPPELGANGER's learning techniques and their implementation in an application-independent, sensor-independent environment.