This volume explores the intersection of robust intelligence (RI)
and trust in autonomous systems across multiple contexts among
autonomous hybrid systems, where hybrids are arbitrary combinations
of humans, machines and robots. To better understand the
relationships between artificial intelligence (AI) and RI in a way
that promotes trust between autonomous systems and human users,
this book explores the underlying theory, mathematics,
computational models, and field applications. It uniquely unifies
the fields of RI and trust and frames it in a broader context,
namely the effective integration of human-autonomous systems. A
description of the current state of the art in RI and trust
introduces the research work in this area. With this foundation,
the chapters further elaborate on key research areas and gaps that
are at the heart of effective human-systems integration, including
workload management, human computer interfaces, team integration
and performance, advanced analytics, behavior modeling, training,
and, lastly, test and evaluation. Written by international leading
researchers from across the field of autonomous systems research,
Robust Intelligence and Trust in Autonomous Systems dedicates
itself to thoroughly examining the challenges and trends of systems
that exhibit RI, the fundamental implications of RI in developing
trusted relationships with present and future autonomous systems,
and the effective human systems integration that must result for
trust to be sustained. Contributing authors: David W. Aha, Jenny
Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael
Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir
Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil
Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul
Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer,
Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron
Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue
Wang, Holly A. Yanco, Dan Zwillinger.
General
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