Author Description

Cyber-Physical Systems (CPS) such as aircraft, automobiles, industrial robots, medical devices, and Internet-of-Things (IoT) applications, promise significant economic and societal benefits. Advances in the area of Artificial Intelligence are promoting a shift of operational responsibilities from humans to systems that redefines them as autonomous cyber-physical systems in the sense that they operate and achieve goals in complex environments that are not fully specified and constrained at design time. A key aspect of the design challenge is to demonstrate the science and effectiveness of the approaches that are making physical sensing and actuation, perception, situational assessment, decision making, and execution possible at runtime. However, this requires assurance that the system as designed will be able to deal with uncertainty, learn from experience, while reacting at the pace of the physical environment. The ability to handle such intractable high-dimensional spaces often relies on Artificial Intelligence tools, where both academia and industry are rapidly developing innovative solutions in the area of data-driven and model-based techniques, as well as their hardware and software implementation. However, CPS and autonomy challenge these design methodologies, as more freedom is left to both the environment and the control policies that can be adapted and evolved over time through learning. ACM/IEEE DESTION provides a premier forum for researchers and engineers from academia, industry, and government to present and discuss challenges, promising solutions, and applications in design automation for Autonomous CPS and IoT. The workshop has a broad scope covering tools for modeling, simulation, synthesis, validation and verification of Autonomous CPS and IoT, and their applications in a variety of domains, such as automotive and transportation systems, avionics, robotics, buildings, grid, and medical devices.