Two common families of approaches to learning agents are either to use weak universal learning (e.g. Reinforcement learning) or strong task-specialized learning and reasoning systems (e.g. case based or explanation based reasoning systems). Here we consider a third path, one that is universal, in the sense of weak learning systems, but takes advantage of several reinforcing feedback signals that permit bootstrapping of strong performance from repeated iterations of weak learning.
Submitted: Sep 15, 2008