Titlebook: Robust Intelligence and Trust in Autonomous Systems; Ranjeev Mittu,Donald Sofge,W.F. Lawless Book 2016 Springer Science+Business Media (ou

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Artificial Brain Systems Based on Neural Network Discrete Chaotic Dynamics. Toward the Development ived herein from first physicochemical principles and applied to neural network operations. In this application, we are assuming that the biochemical reactions are accompanied and controlled by an “information exchange” between neurons, neural networks and the different types of neural networks resp

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Investigating Human-Robot Trust in Emergency Scenarios: Methodological Lessons Learned,s one might think. The perceived risk involved in a scenario and the precise wording of a question can bias the outcome of a study in ways that the experimenter did not intend. This chapter presents the lessons we have learned about trust while conducting human-robot experiments with 770 human subje

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Designing for Robust and Effective Teamwork in Human-Agent Teams, teams. In applications such as search and rescue, command and control, and air traffic control, operators in the future will likely need to work in teams together with robots. It is critical to understand how these teams could be robust against uncertainty and what influences team performance. We c

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The Intersection of Robust Intelligence and Trust: Hybrid Teams, Firms and Systems,ms (i.e., arbitrary combinations of humans, robots and machines), which cannot be done presently. Uncertainty is created in states of interdependence between social objects: at one extreme, interdependence reduces to independent agents and certainty but with asocial, low-power solutions generating l

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,The “Trust V”: Building and Measuring Trust in Autonomous Systems,s systems. These methods are used throughout a product’s lifecycle. This paradigm led us to invent new methods that create both systems and operational trust for autonomous systems. Several examples of these new methods, that are useful for all systems, are given.