Digital Evolution of Behavioral Models for Autonomic Systems

We describe an automated method to generating models of an autonomic system. Specifically, we generate UML state diagrams for a set of interacting objects, including the extension of existing state diagrams to support new behavior. The approach is based on digital evolution, a form of evolutionary computation that enables a designer to explore an enormous solution space for complex problems. In our application of this technology, an evolving population of digital organisms is subjected to natural selection, where organisms are rewarded for generating state diagrams that support key scenarios and satisfy critical properties as specified by the developer. To achieve this capability, we extended the Avida digital evolution platform to enable state diagram generation, and integrated AviDA with third-party software engineering tools, e.g., the Spin model checker, to assess the generated state diagrams. To illustrate this approach, we successfully applied it to the generation of state diagrams describing the autonomous navigation behavior of a humanoid robot.