Autonomous and Adaptative Cyber-Physical Systems (ACPS) represent a new knowledge frontier of converging “nano-bio-info-cogno” technologies and applications. ACPS have the ability to integrate new ‘mutagenic’ technologies, i.e., technologies able to cause mutations in the society. Emerging approaches, such as artificial intelligence techniques and deep learning, enable exponential speedups for supporting increasingly higher levels of autonomy and self-adaptation. In spite of this disruptive landscape, however, deployment and broader adoption of ACPS in safety-critical scenarios remains challenging. In this paper, we address some challenges that are stretching the limits of ACPS safety engineering, including tightly related aspects such as ethics and resilience. We argue that a paradigm change is needed that includes the entire socio-technical aspects, including trustworthiness, responsibility, liability, as well as the ACPS ability to learn from past events, anticipate long-term threads and recover from unexpected behaviors.

We, the authors, wish to make the following corrections to our paper

Robots need task planning to sequence and execute actions toward achieving their goals. On the other hand, Behavior Trees provide a mathematical model for specifying plan execution in an intrinsically composable, reactive, and robust way. PDDL (Planning Domain Definition Language) has become the standard description language for most planners. In this paper, we present a novel algorithm to systematically create behavior trees from PDDL plans to execute them. This approach uses the execution graph of the plan to generate a behavior tree. The most remarkable contribution of this approach is the algorithm to build a Behavior Tree that optimizes its execution by paralyzing actions, applicable to any plan, taking into account the actions' causal relationships. We demonstrate the improvement in the execution of plans in mobile robots using the ROS2 Planning System framework.