This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. The system has the ability to predict the users' intended destination at a larger scale, that of a typical office or home arena. This system relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently aid in driving the user to the destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users' focus from fine motor-skilled control to coarse guidance, broadly intended to convey intention. Successful simulation and experimental results on a real automated wheelchair platform demonstrate the validity of the approach.