MOSES is a machine-learning tool; it is an "evolutionary program learner". It is capable of learning short programs that capture patterns in input datasets. These programs can be output in either the combo programming language, or in python. For a given data input, the programs will roughly recreate the dataset on which they were trained. MOSES has been used in several commercial applications, including the analysis of medical patient and physician clinical data, and in several different financial systems. It is also used by OpenCog to learn automated behaviors, movements and actions in response to perceptual stimulus of artificial-life virtual agents (i.e. pet-dog game avatars). Future plans including using it to learn behavioral programs that control real-world robots, via the OpenPsi implementation of Psi-theory and ROS nodes running on the OpenCog AtomSpace.