Self-organization at the lowest level: proactively learning skills in autonomous systems

Abstract

To enable autonomous systems to learn basic skills for unknown and changing environments and stay robust in case of change, Organic Computing principles have to be applied at all layers. In this work an architecture is presented that can be used at the lowest layer providing robust skills to higher-level strategy layers, that depend on encapsulated actions. With emphasis on robustness it is able to learn to control its actors without a priori information about their meaning. This is made possible by skill modules that are learned together with their action-effect dependencies and their enabling preconditions by proactively carrying out experiments within their environment. The architecture is evaluated by simulating a differentially driven robot.