Enabling Collaborative Human-Machine (H-M) Decision Making in Time-critical Activities

Metadata Updated: February 28, 2019

Self-adjusting autonomous systems (SAS) are spreading from well-defined control activities, such as manufacturing, to complex activities with multi-faceted human interactions and decision making, such as those involved in piloting an aircraft. SAS[HTML_REMOVED] ability to solve large problems of certain types far exceeds that of humans[HTML_REMOVED]: problems with millions of variables and constraints are tractable for machines, while human decision making is far more limited in scope. However, an automated solution is only as good as the problem statement, including its completeness. Until SAS are proven and perceived to be as or more adaptable than humans, and resilient in the face of unanticipated (and therefore not included in decision making models) faults and variable conditions, humans will have to remain in ultimate control of decision making, while supported by machine-based information and advice. H-M interaction in many domains has numerous well-known and well-documented difficulties, including lack or excess of trust, both of which can lead to serious problems, especially in time-critical and safety-critical situations where human decision makers quickly become overwhelmed with information.[HTML_REMOVED] In these situations, humans become either reluctant to take advice from machines or lose situational awareness and basic skills via overreliance on machines. Our overarching objective is to develop a concept and an associated software-enabled mechanism that will support real-time decision making in time-critical and safety-critical activities.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_34921
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 766f2c0c-3bf4-42d8-9208-94ac6afc74a2
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2016-09-01
Homepage URL https://techport.nasa.gov/view/34921
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash a41e8215b838396392d3da20132f0688cf11c1c2
Source Schema Version 1.1

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