  Long-term autonomy of robotic systems implicitly requires dependable
platforms that are able to naturally handle hardware and software faults,
problems in behaviors, or lack of knowledge. Model-based dependable platforms
additionally require the application of rigorous methodologies during the
system development, including the use of correct-by-construction techniques to
implement robot behaviors. As the level of autonomy in robots increases, so do
the cost of offering guarantees about the dependability of the system.
Certifiable dependability of autonomous robots, we argue, can benefit from
formal models of the integration of several cognitive functions, knowledge
processing, reasoning, and meta-reasoning. Here we put forward the case for a
generative model of cognitive architectures for autonomous robotic agents that
subscribes to the principles of model-based engineering and certifiable
dependability, autonomic computing, and knowledge-enabled robotics.
