Presenter
Rongbing
Xu,
Master's
student,
Department
of
Systems
Design
Engineering
Abstract
Cognitive
architecture
models
can
support
the
simulation
and
prediction
of
human
performance
in
complex
human-machine
systems.
In
the
current
work,
we
demonstrate
a
pilot
model
that
can
perform
and
simulate
taxiing
and
takeoff
tasks.
The
model
was
built
in
Queueing
Network-Adaptive
Control
of
Thought
Rational
(QN-ACTR)
cognitive
architecture
and
can
be
connected
to
flight
simulators
such
as
X-Plane
to
generate
various
data
including
performance,
mental
workload,
and
situation
awareness.
The
model
results
are
determined
in
combination
by
the
declarative
knowledge
chunks,
production
rules,
and
a
set
of
parameters.
Currently,
the
model
can
generate
flight
operation
behavior
similar
to
human
pilots,
We
will
collect
human
pilot
data
to
further
examine
and
validate
model
assumptions
and
parameter
values.
Once
validated,
such
models
can
support
interface
evaluation
and
competency-based
pilot
training,
providing
a
theory-based
predictive
approach
complementary
to
human-in-the-loop
experiments
for
aviation
research
and
development.
Location
Microsoft
Teams
meeting
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