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

Metadata Updated: November 12, 2020

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’ ability to solve large problems of certain types far exceeds that of humans’: 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.  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.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Space Technology Mission Directorate
Unique Identifier Unknown
Identifier TECHPORT_34921
Data First Published 2016-09-01
Data Last Modified 2020-01-29
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id be82204f-0f29-4eaf-9307-0c8e2c3ee367
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:027
Source Datajson Identifier True
Source Hash 23e78f721aea619a7f5bb47b44d9ec8eee9c885f
Source Schema Version 1.1

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