Title:
Towards Optimal Human-Machine Teaming

dc.contributor.author Clarke, John-Paul B.
dc.contributor.corporatename Georgia Institute of Technology. School of Aerospace Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Industrial and Systems Engineering en_US
dc.date.accessioned 2020-06-15T14:27:52Z
dc.date.available 2020-06-15T14:27:52Z
dc.date.issued 2019-07-29
dc.description Plenary Talk during "Technical Session 1: Human machine interaction (HMI)" of the 2019 AIAA Intelligent Systems Workshop held at the University of Cincinnati. en_US
dc.description.abstract Machines were initially designed to take over manual tasks. Over time, as sensors and algorithms became more capable, decision-making has been automated via prescribed rules. Further, when these rules can be verified a priori, automated systems are allowed to operate autonomously, i.e., without human supervision. Some envision that in due time machines will be able to both make decisions and operate autonomously. That said, despite science-fiction-inspired anxieties, it is unlikely that machines will replace humans in their entirety. Rather, the future will be one where humans and machines work together in partnership to achieve performance that is greater than the performance they could achieve individually. Specifically, the optimal allocation of functions to humans and machines will be dependent on their relative strengths with respect to autonomous decision-making and autonomous operation. In this presentation, I will present a framework for determining the optimum allocation of functions to humans and machines; and provide specific instances where each of the four possible allocations is optimal. en_US
dc.description.sponsorship United Technologies Corporation en_US
dc.identifier.uri http://hdl.handle.net/1853/62917
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Autonomy en_US
dc.subject Human-machine teaming en_US
dc.title Towards Optimal Human-Machine Teaming en_US
dc.type Text
dc.type.genre Presentation
dspace.entity.type Publication
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
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