Computation is everywhere in our modern lives. Currently, the vast majority of non-biological computation is done using discrete symbols that we have created to represent entities in our environment. Dates, sales figures, airport codes–all of these entities were created by humans and are hand coded to be easily represented by computers. But in the future, computing will be increasingly applied to the physical world [1]. This computing will often be applied through cyber-physical systems that tightly couple computation and physical resources [2]. This shift will lead to two challenges: (1) handling the explosion of unformatted data, and (2) handling the explosion of system size. The explosion of data will come from increasingly ubiquitous cameras and sensor networks. The explosion of system size will come from the proliferation of robots [3]. As the capability of robots expands, we will need autonomous learning because it will be increasingly difficult to program them in any other way. Essential for handling both types of problems will be the autonomous learning of patterns. Patterns allow learning agents to summarize a large amount of low-level information into one chunk. Patterns can be perceptual patterns or action patterns. A perceptual pattern, once identified, can be pulled out of the large sensory input stream and be used to make predictions. An action pattern can be used to bring about a desired effect in the environment.