About the Book
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy.
The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
Table of Contents:
W.F. Lawless, R. Mittu, D.A. Sofge, T. Shortell and T.A. McDermott, Jr.: Introduction to the Subject and the Chapters.- P. Cummings, N. Schurr, A. Naber, Charlie and D. Serfaty: Recognizing Artificial Intelligence: The Key to Unlocking Human AI Teams.- T.A. McDermott, M.R. Blackburn and P.A. Beling: Artificial Intelligence and Future of Systems Engineering.- N.J. Cooke and W.F. Lawless: Effective Human-Artificial Intelligence Teaming.- M. Steinberg: Towards Systems Theoretical Foundations for Human-Autonomy Teams.- J. Llinas, R. Mittu and H. Fouad: Systems Engineering for Artificial Intelligence-based Systems: A Review in Time.- K.E. Schaefer, B. Perelman, J. Rexwinkle, J. Canady, C. Neubauer, N. Waytowich, G. Larkin, K. Cox, M. Geuss, G. Gremillion, J.S. Metcalfe, A. DeCostanza and A. Marathe: Human-Autonomy Teaming for the Tactical Edge: The Importance of Humans in Artificial Intelligence Research and Development.- Stephen Russell, B. Jalaian and I.S. Moskowitz: Re-orienting towardsthe Science of the Artificial: Engineering AI Systems.- M. Sheehan and O. Yakimenko: The Department of Navy’s Digital Transformation with the Digital System Architecture, Strangler Patterns, Machine Learning, and Autonomous Human-Machine Teaming.- M. Mylrea, M. Nielsen, J. John and M. Abbaszade: AI Driven Cyber Physical Industrial Immune System for Defenders of Critical Energy Infrastructure.- I.S. Moskowitz, N.L. Brown and Z. Goldstein: A fractional Brownian Motion Approach to Psychological and Team Diffusion Problems.- P. Deignan: Human-Machine Understanding: The Utility of Causal Models and Counterfactuals.- M. Garcia, T. Goranson and B. Cardier: An Executive for Autonomous Systems, Inspired by Fear Memory Extinction.- E. Santos Jr., C. Nyanhongoa, H. Nguyen, K. Joo Kima, Gr. Hydea: Contextual Evaluation of Human-Machine Team Effectiveness.- S.-H. Chen: Humanity in the Era of Autonomous Human-Machine Teams.- J C. Wood, W F Lawless: Transforming the system of military medical research: An Institutional History of the Department of Defense’s (DoD) First Electronic Institutional Review Board Enterprise IT System.- N. Peters, M. Ugolini and G. Bowers: Collaborative communication and intelligent interruption systems.- N. Shadad, A.U. Kulkarni and S. Alejandro: Shifting Paradigms in Verification and Validation of AI-Enabled Systems: A Systems-Theoretic perspective.- A.D. Cobb, B. Jalaian, N.D. Bastian and S. Russell: Towards Safe Decision-Making via Uncertainty Quantification in Machine Learning.- M. Wollowski, L. Chen, X. Chen, Y. Cui, J. Knierman and X. Liu: Engineering Context from the Ground Up.- N. Hili, A. Albore and J. Baclet: From Informal Sketches to Systems Engineering Models Using AI Plan Recognition.- R.P. Quandt: An Analogy of Sentence Mood and Use.- M. Akiyoshi, J. Whitton, I. Charnley-Parryc and W.F. Lawless: Effective Decision Rules for Systems of Public Engagement in Radioactive Waste Disposal: Evidence from the United States, the United Kingdom, and Japan.- B. Cardier, A. Nieslen, J. Shull and L. Sanford: Outside the Lines: Visualizing Influence Across Heterogenous Contexts in PTSD.
