  Goals express agents' intentions and allow them to organize their behavior
based on low-dimensional abstractions of high-dimensional world states. How can
agents develop such goals autonomously? This paper proposes a detailed
conceptual and computational account to this longstanding problem. We argue to
consider goals as high-level abstractions of lower-level intention mechanisms
such as rewards and values, and point out that goals need to be considered
alongside with a detection of the own actions' effects. We propose Latent Goal
Analysis as a computational learning formulation thereof, and show
constructively that any reward or value function can by explained by goals and
such self-detection as latent mechanisms. We first show that learned goals
provide a highly effective dimensionality reduction in a practical
reinforcement learning problem. Then, we investigate a developmental scenario
in which entirely task-unspecific rewards induced by visual saliency lead to
self and goal representations that constitute goal-directed reaching.
