Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc

Online Optimisation of Casualty Processing in Major Incident Response

Recent emergency response operations to Mass Casualty Incidents (MCIs) have been criticised for a lack of coordination, implying that there is clear potential for response operations to be improved and for corresponding benefits in terms of the health and well-being of those affected by such incidents. In this thesis, the use of mathematical modelling, and in particular optimisation, is considered as a means with which to help improve the coordination of MCI response. Upon reviewing the nature of decision making in MCIs and other disaster response operations in practice, this work demonstrates through an in-depth review of the available academic literature that an important problem has yet to be modelled and solved using an optimisation methodology. This thesis involves the development of such a model, identifying an appropriate task scheduling formulation of the decision problem and a number of objective functions corresponding to the goals of the MCI response decision makers. Efficient solution methodologies are developed to allow for solutions to the model, and therefore to the MCI response operation, to be found in a timely manner. Following on from the development of the optimisation model, the dynamic and uncertain nature of the MCI response environment is considered in detail. Highlighting the lack of relevant research considering this important aspect of the problem, the optimisation model is extended to allow for its use in real-time. In order to allow for the utility of the model to be thoroughly examined, a complementary simulation is developed and an interface allowing for its communication with the optimisation model specified. Extensive computational experiments are reported, demonstrating both the danger of developing and applying optimisation models under a set of unrealistic assumptions, and the potential for the model developed in this work to deliver improvements in MCI response operations

EXPLORING THE POTENTIAL OF A MACHINE TEAMMATE

Artificial intelligence has been in use for decades. It is already deployed in manned formations and will continue to be fielded to military units over the next several years. Current strategies and operational concepts call for increased use of artificial-intelligence capabilities across the defense enterprise—from senior leaders to the tactical edge. Unfortunately, artificial intelligence and the warriors that they support will not be compatible "out of the box." Simply bolting an artificial intelligence into teams of humans will not ensure success. The Department of Defense must pay careful attention to how it is deploying artificial intelligences alongside humans. This is especially true in teams where the structure of the team and the behaviors of its members can make or break performance. Because humans and machines work differently, teams should be designed to leverage the strengths of each partner. Team designs should account for the inherent strengths of the machine partner and use them to shore up human weaknesses. This study contributes to the body of knowledge by submitting novel conceptual models that capture the desired team behaviors of humans and machines when operating in human-machine teaming constructs. These models may inform the design of human-machine teams in ways that improve team performance and agility.NPS_Cruser, Monterey, CA 93943Outstanding ThesisMajor, United States Marine CorpsMajor, United States Marine CorpsApproved for public release. Distribution is unlimited