Automation Lecture - Peter Hancock

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Nov 5, 2013 (3 years and 9 months ago)

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Hancock

Interaction with Automation

1


A
DAPTIVE

A
UTOMATION


Instructor
:
Dr.
Peter Hancock


Lecture Overview


In the next three hours we are going to
seek to

cover

three
complement
ary

topics.


-

The use of automation in general, when is it appropriate to use automation, is it always
appropriate
and what are the goals and desired (hierarchical?) relations between humans and
machines.


-

Define and approach problems and promises in Adaptive Automation.


-

Define adaptable interfaces and the difference between adaptable and adaptive.


Unfortunately,

each one of these topics can be the focus of an entire course or at least
deserves a
lecture of its own. It is therefore important that you use the lecture reading list as a “starter” to get
you more familiar with the various topics we will discuss in cla
ss. Here again I have chosen key
articles, which I hope will lead you to others.


Some concept in automation

that we will discuss briefly in class are:



MABA
-
MABA



Supervisory control



Level of automation



Level of control


management by consent MBC or manage
ment by exception
MBE



Humans and automation: Use, misuse, disuse, abuse (after Parasuraman & Riley,
1997)


What
is adaptive automation?


Adaptive automation (AA) is an approach to automation design where tasks are
dynamically allocated over time between hu
mans and machines in cooperative systems
for the purpose of optimizing overall system performance.


The main goal of research in AA is to give solutions to underpinnings of automation by
means of the development of systems that properly adaptively allocat
e tasks to either
Hancock

Interaction with Automation

2


humans, machines, or both. One can distinguish two main research questions in AA that
together might offer us such solutions:

(1)
Effects of
automation on overall system performance

This is mainly an empirical driven effort. T
rust, skill
, workload, situation awareness,
mode awareness, over
-

and underuse, boredom, stress, and automation
-
induced
complacency.

(2)
D
etermining successful
triggering mechanisms and transition criteria

This
is a theory driven
-
effort
. Such as theories on transpare
ncy, machine autonomy, task
switching, responsibility, "human in the loop"
-
ness, and delegation strategy.

Some challenges:



C
an machines learn to cooperate with humans?

Increasing intelligence of machines leads to a shift of human
-
machine interaction
to hu
man
-
machine cooperation. There is a need for humans and machines to
comprehend each other's reasoning and behavior (first mentioned in [Hollnagel,
Woods, 1983]). If the machines were replaced by humans, this would be
explained as a need for cooperation [Ho
c, 2001]. An interesting idea is to have the
machine learn to cooperate with humans. In order to be able to do that, a machine
should be able to assess and adapt goals of a human.



How does one measure "human in the loop"
-
ness and estimate its desired level
?

Automation has the purpose to let machines do what formerly humans did equally
or less effective. One would expect engineers to automate as much as possible in
that respect. But an important disadvantage of it is that in particular situations the
cooper
ative human is now too much "out of the loop" and has, in case of an
incident, a limited situation awareness and therefore is not able to cope with the
situation. A challenge is to find out for each of those situations how to measure
"human in the loop"
-
ne
ss, estimate its desired level, and to reach to that.



How do trust and transparency relate in human
-
machine cooperation?

Adaptive automation results in humans and machines to get in or lose control. If
such reallocations of control are not expected to be

effective by either a human or
machine, the allocations are likely to be overruled because of a lack of trust in the
other, itself, or the allocator. In the case that such lack of trust is unnecessary,
ways to support agents to correctly trust each other
are welcome. For instance,
this can be done by automatically adapting the transparency level of the different
task execution processes.

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Interaction with Automation

3


Adaptable I
nterfaces

Adaptive interfaces are a relatively new attempt to overcome contemporary problems due
to the incre
asing complexity of human
-
computer interaction. They are designed to tailor a
system's interactive behavior with consideration of both individual needs of human users
and altering conditions within an application environment. The broader approach of
intell
igent user interfaces includes adaptive characteristics as a major source of its
intelligent behavior. In dynamic situations where task requirements and operator states
can change from moment to moment, such as those found in battle conflicts or agile work

environments, a fixed interface only provides the best mappings between the user and
technology over the narrow predefined range fixed during design, producing suboptimal
performance outside of this design envelope. Given the very dynamic and ever
-
changin
g
situations addressed by both military and global industry, a real
-
time rapidly
customizable interface between the human and technology is required to continuously
maintain the best match between these entities for maximizing both the personnel and
techno
logy investments.

To develop an adaptive model that meets a set of criteria, the model must:



incorporate representations of operator states.



represent interface features which are adaptable.



represent cognitive processing in a computational framework.



provide measures of merit for matches between the input state variables and
interface adaptation permutations.



operate in a real
-
time mode.



incorporate self
-
evolving mechanisms.


Current Learning Objectives




To understand
the
basic concepts in
automate
d
human
-
machine systems



To understand how human operators are affected by automation
implementation
in real
-
world systems.



To comprehend how the uses of human performance measurement may be used to
enhance human
-
machine system design.


Lecture Readings


Automation



Kirlik, A. (1993), Modeling strategic behavior in human
-
automation interaction: Why an “aid" can (and
should) go unused.
Human Factors, 34

(2), 221
-
242.


Parasuraman, R. & Riley, V. (1997). Humans and automation: Use, misuse, disuse and abuse
,
Human Factors
,
39
, 230
-
253.


Sheridan,

T.B.
(2002).
Humans and automation: System design and research issues
,

Wiley: Santa Monica,
CA,.


Sheridan,

T.B.

(
1992
).
Telerobotics, automation and
supervisory

control
, MIT: Cambridge MA,


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Interaction with Automation

4


Adaptive Automation



Foundations


Byrne, E. & Parasuraman, R. (1996) Psychophysiology and adaptive automation,
Biological Psychology
,
42, 249
-
268.


Hancock
,
P.A.
&

Chignell,

M.H.
(1987).
Adaptive c
ontrol in human
-
machine systems.

I
n
Human
f
actors
p
sychology,

(pp. 305
-
345)
P.A.

Hancock,
(
Ed
.)
, Amsterdam: North
-
Holland, 305
-
345.


Hancock,
P.A.
&

Scallen
, S.F.

(1996).

The future of function allocation
.

Ergonomics in Design, 4 (4)
,

24
-
29
.



Hollnagel
, E.
,
&
Woods
, D.D. (1983). Cognitive systems engineering: New wine in new b
ottles
.
International Journal of Man
-
Machine Studies
,

18 (6)
,

583
-
600
.


Parasuraman, R., Mouloua, M., & Molloy, R. (1996).
Effects of adaptive task allocation on monitoring of
automated systems.
Human Factors, 38,

665
-
679.


Rouse
,
W.B.
(1994).
Twenty years of ad
aptive aiding: origins of the concept and lessons learned. In: M.
Mouloua and R. Parasuraman. Eds.,
Human performance in automated systems: Current research
and trends
.
(
pp. 2
8
-
32
), Hillsdale, NJ, Erlbaum.


Wiener,
E.L.

(
1973)

Adaptive mea
surement of vigil
ance decrement.

Ergonomics
,

16
, 353
-
363.



Adaptive Automation



Recent Work


Hoc
,
J.
M.
(2001). Towards a cognitive approach to human
-
m
achine
c
ooperation in
d
ynamic
s
ituations,
International Journal of Human
-
Computer Studies
,
54(4)
,
509
-
540.

Inagaki, T. (2
003). Adaptive Automation: Sharing and Trading of Control. In: E. Hollnagel (Ed.),
Handbook of Cognitive Task Design.

(pp. 147
-
169), Lawrence Erlbaum Associates. Mahwah, NJ.

Parasuraman, R., Sheridan T., & Wickens, C. (2000). A model for types and levels o
f human interaction with
automation.
IEEE Transactions on Systems, Man, and Cybernetics, 30
, 286
-
297
.


Adaptable Interfaces


http://www.sift.info/English/publications/PD
F/MFGWM
-
AugCog
-
DecMak.pdf


Reinhard Oppermann

(Ed.) (1994).
Adaptive User Support
,
Ergonomic Design of Manually and
Automatically Adaptable Software Institute for Applied Information Technology GMD,

L
EA Publishers:

Hillsdale, New Jersey Hove, UK

available

online at
http://www.questia.com/PM.qst?a=o&d=78542879