SPECIAL FEATURE MARCH 2O20 VOLUME 23/ ISSUE 1 8 AI4SE and SE4AI: A Research Roadmap INTRODUCTION S ystems engineering is undergoing a digital transformation that will lead to transformational advances facilitating systems engineering use of artifcial intelligence (AI) and machine learning (ML) technology to automate many routine engineering tasks. At the same time applying AI, ML, and autonomy to complex and critical systems encourages new systems engineering methods, processes, and tools. A 2019 future of systems engineering (FuSE) workshop, hosted by the International Council on Systems Engineering (INCOSE), frst used the terms AI for systems engineering and systems engineering for AI to describe this dual transformation (Miller 2019). Te “AI4SE” and “SE4AI” labels have become metaphors for an upcoming rapid evolutionary phase in the systems engineering Community. AI4SE applies augmented intelligence and machine learning techniques to support systems engineering practices. Goals in such applications include achieving scale in model construction and confdence in design space exploration. SE4AI applies systems engineering methods to learning- based systems’ design and operation. Key research application areas include developing principles for learning-based systems design, life cycle evolution models, and model curation methods. To better understand and focus on this evolution, the Systems Engineering Research Center’s (SERC) research council, Figure 1. SERC research areas and missions a US Defense Department sponsored uni- versity afliated research center (UARC), developed a roadmap to structure and guide research in Artifcial Intelligence (AI) and autonomy. Tis paper presents that roadmap. Te SERC research strategy aligns three mission areas supported by four research areas, shown in Figure 1. Te research areas are enterprises and systems of systems (ESOS), trusted systems (TS), systems engineering and systems management transformation (SEMT), and human capital development (HCD). Te mission areas the SERC is addressing are: ■ Velocity: Developing and sustaining timely capabilities supporting emergent and evolving mission objectives (deter and defeat emergent and evolving adversarial threats and exploit oppor- tunities afordably and with increased efciency). ■ Security: Designing and sustaining the demonstrable ability to safeguard critical technologies and mission capa- bilities in the face of dynamic (cyber) adversaries. VELOCITY Developing and sustaining capabilities that support emergent and evolving mission objectives (deter and defeat emergent and evolving adversarial threats and exploit opportunities, affordably and with increased ef fciency) SECURITY Designing and sustaining the demonstrable ability to safeguard critical technologies and mission capabilities in the face of dynamic (cyber) adversaries AI & AUTONOMY Developing and supporting system engineering MPTs to understand, exploit, and accelerate the use of AI and autonomy in critical capabilities TRUSTED SYSTEMS ENTERPRISES AND SYSTEMS OF SYSTEMS SYSTEMS ENGINEERING AND MANAGMENT TRANSFORMATION HUMAN CAPITAL DEVELOPMENT Mission Engineering Digital Engineering SERC Technical Plan Roadmaps S V A  ABSTRACT In 2019, the Research Council of the Systems Engineering Research Center (SERC), a US Defense Department sponsored Univer- sity Afliated Research Center (UARC), developed a roadmap structuring and guiding artifcial intelligence (AI) and autonomy research. Tis paper presents that roadmap and key underlying Digital Engineering transformation aspects both enabling tradi- tional systems engineering practice automation (AI4SE), and encourage new systems engineering practices supporting a new wave of automated, adaptive, and learning systems (SE4AI).  KEYWORDS: systems engineering, artifcial intelligence, machine learning, automation, research Tom McDermott, tmcdermo@stevens.edu; Dan DeLaurentis, ddelaure@purdue.edu; Peter Beling, beling@virginia.edu; Mark Blackburn, mblackbu@stevens.edu; and Mary Bone, mbone@stevens.edu Copyright © 2020 by Tom McDermott, Dan DeLaurentis, Peter Beling, Mark Blackburn, and Mary Bone. Published and used by INCOSE with permission.