WEBVTT

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Greetings.

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This video will offer
information on the STTR

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subtopic, Integrated Data
Uncertainty Management and

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Representation for Trustworthy
and Trusted Autonomy in Space.

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My name is Natalie Alexandrov.

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I work in design and analysis of
complex airspace systems with

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emphasis on the trustworthiness
of the human machine teams,

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where intelligent computational
agents such as machine learning

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models work alongside human team
members.

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With increased reliance on
autonomous machines, it's

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critical to understand the
reliability of their decision

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making, which includes
reliability of both data and

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decision making algorithms.

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This in turn means understanding
uncertainties associated with

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the use of data and
computational models, especially

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in safety critical domains such
as airspace representation and

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management of uncertainties in
human machine teams is the focus

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of this subtopic.

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Uncertainty management in
technical systems has

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traditionally been done with the
robust and reliability based

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design that rely on physics
based models.

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Expansion to human machine teams
will rely on well known

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approaches but must incorporate
additional considerations.

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Given increased machine autonomy
and inclusion of machine

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learning, we must understand
uncertainty in the data such as

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accuracy and integrity from a
single source, as well as

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combined uncertainty of the data
from multiple sources.

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We must also detect anomalies
during operations, verify and

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validate models during training,
during operations and following

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operations, and also have a
rigorous representation of

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uncertainties for analysis, as
well as a representation of

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uncertainties in the form that
is understandable to human

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operators.

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These uncertainties cover a
large range of problems, so

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proposers should choose an
aspect or a set of aspects of

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managing uncertainties described
in the solicitation and offer a

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general methodology for
achieving A chosen aspect of

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uncertainty management.

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They should also offer a
specific application of the

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approach relevant to NASA
missions.

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Traditional, well studied
operations are of two kinds.

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Completely automatic operations,
such as those of an assembly

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line, operate in prescribed
structured conditions with

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specific if then else clauses
for decisions and actions in

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complex systems such as air
traffic control or piloting an

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aircraft.

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While many functions are
automated, the ultimate control

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and responsibility lie with
human operators.

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Current developments of
driverless vehicles still lack

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the degree of verification,
validation and safety assurance

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necessary for safety, critical
space and airspace applications.

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Moreover, there is a great gap
in rigorous uncertainty

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management in human machine
teams where humans and machines

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are participants rather than
predetermined controllers.

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Uncertainty management is a
completely cross cutting

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research area with outcomes of
interest to all NASA missions.

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Operation on future orbiting
stations or surface stations are

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potential example demonstration
platforms.

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The environment there would
include a variety of autonomous

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systems used for habitat
maintenance when the station is

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uninhabited.

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It would also include continual
system health management, crew

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health monitoring, robotic
assembly, and cyber security,

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among other functions.

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The proposers may choose any
relevant design reference

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mission.

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However, they should also
substantiate the

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generalizability and scalability
of the approach.

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Again, sound uncertainty
management is relevant in all

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areas where operations are
controlled by computational

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software or human machine teams.

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For example, the autonomous
ground traffic under development

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still lacks proof of safety.

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Data based models are used in
medical diagnosis and prognosis

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where uncertainty quantification
is essential and still an open

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question.

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The projected autonomous air
traffic is in need of rigorous

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validation and verification
methods, so any general and

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sound uncertainty representation
and management methodology and

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the attendant tools may find
good use in a variety of

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domains.

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Finally, thank you for your
interest.