Robust autonomous systems will need to be adaptable to changes in the environment and changes in the underlying physical system. This is especially critical for long-duration missions. For example, autonomous robots that explore other planets for years will need to adapt to degradation in their capabilities and to unforeseen environmental factors. Another NASA domain requiring robust, adaptable autonomy is control of closed-loop systems that will provide life support to crews on long-duration missions. We have been investigating autonomous control of advanced life support systems for many years (Schreckenghost et al. 1998; Kortenkamp, Keirn-Schreckenghost, and Bonasso 2000). Recently we have begun investigating learning with respect to advanced life support systems (Kortenkamp, Bonasso, and Subramanian 2001). In this abstract briefly discuss some of the roles machine learning can play with respect to control of advanced life support systems or any complex, real-time system.