A significant emphasis in trying to achieve adaptation and learning in the perception-action cycle of agents lies in the development of suitable algorithms. While partly these algorithms result from mathematical constructions, in modern research much attention is given to methods that mimic biological processes. However, mimicking the apparent features of what appears to be a biologically relevant mechanism makes it difficult to separate the essentials of adaptation and learning from accidents of evolution. This is a challenge both for the understanding of biological systems as well as for the design of artificial ones. Therefore, recent work is increasingly concentrating on identifying general principles rather than individual mechanisms for biologically relevant information processing. One advantage is that a small selection of principles can give rise to a variety of - effectively equivalent - mechanisms. The ultimate goal is to attain a more transparent and unified view on the phenomena in question. Possible candidates for such principles governing the dynamics of the perception-action cycle include but are not limited to information theory, Bayesian models, energy-based concepts or group-theoretical principles. The workshops aims at bringing together various principle-based directions for the investigation of various aspects of the perception-action cycle and at identifying promising directions of work.