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OCR for page 52
Applications
o
This chapter discusses the application of human performance mod-
els (HPMs) to four classes of real-world problem areas. The first two
concern human-machine interaction in relatively well~efined operational
situations: piloting of aircraft and control room operation of nuclear power
plants. The third category concerns maintenance a type of activity which,
although relatively well defined and economically critical, has been some-
what neglected. The fourth is a broad class of human-machine interactions
wherein the human operator does not perform the task directly, but instead
supervises one or more automatic control systems that execute the direct
control. The latter area, which includes autopilots in aircraft, semiauto-
mated nuclear or chemical plant control, and robots in factories, space, or
undersea, poses new challenges for human performance modeling.
In the following sections it may seem that certain HPM approaches
are constrained to specific application areas (i.e., that procedure/task
network and reliability models are specific to the needs of the nuclear
power industry or that information processing models are specific to the
needs of cockpit designers). This is not the case. The appearance is due
to the fact that each methodology was developed initially for a specific
area of application. Most of the models discussed in this report are being
expanded to other areas, but it is still reasonable to expect to find more
instances of a model's use within its area of origin than outside. This does
not necessarily imply either that a model, or an approach to modeling,
is the only appropriate choice for a particular application or that it is an
inappropriate choice for some other application.
52
OCR for page 53
APPLICATIONS
HUMAN PERFORMANCE MODELS IN AIRCRAFT OPERATIONS
53
The rapid development of aircraft during World War II gave rise to
increasing problems for aircrew members. By the late 1950s, significant
analytical efforts were underway in three human-machine areas that had
been especially affected by changes in aircraft design and their missions:
1. flight control problems associated with new flight regimes and
modified handling qualities;
2. crew workload problems associated with an expansion of mission
requirements and a proliferation of aircraft subsystems with their cor-
responding displays and controls, and aggravated by generally shortened
response times available to the crew, and
3. air-to-surface search and targeting problems associated with new
flight regimes, new sensors, and unproved surface-to-air defenses.
Each of these areas Is treated briefly in the following pages, with
reference to summary documents for more details.
Flight Control
Background
The expansion of operational envelopes and mission requirements for
flight vehicles that occurred in the past two to three decades, and the
resulting increase in task difficulty and pilot workload, have shmulated a
strong need for systematic means of analyzing the pilot-vehicle system and
predicting closed-loop performance and workload.
This, in turn, has led to substantial efforts aimed at developing quan-
titative engineering models for the human pilot performing closed-loop
manual control tasks. As a result of these efforts, there exist an exten-
sive HPM-directed data base, two well-established HPMs for continuous
manual control tasks, and a long list of applications of these models in
the flight control arena. Applications of these models include display and
control system analysis, flight director and stability augmentation system
design, analysis of vehicle-handling qualities, analysis of the limits of piloted
control, analysis of pilot workload, and determination of flight simulator
requirements.
A useful, alternative categorization of these applications that empha-
sizes pilot-vehicle system problems addressable by HPMs for the human
controller is to relate them to flight test, design, and simulator planning
problems; this was done by McRuer and Krendel (1974) and, more recently,
by Ashkenas (1984~. Each of these references provides three tables that
illustrate quite succinctly the broad scope of application of human per-
formance modeling to aircraft control-related problems. Ashkenas (1984)
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54
QU~TAT~ MODELING OF ~~ PENCE
also provides a reference list by application category. These references
focus principally on applications of the quasilinear modeling approach.
Applications of the optimal control model (OCM) and related monitoring
and decision-making models are indicated in Baron and Levison (1977,
1980) and Rouse (1980~. A major source of references on the application
of these and a varieW of other HPMs to control problems is the series
of proceedings from the NASA-un~versity conferences on manual control
(1967-present).
Glenn and Doane (1981) used the Human Operator Simulator (HOS)
to simulate pilot eye-scan behavior during manual, as well as more auto-
mated, flight control modes for both s~aight-in and curved approaches to
landings of a NASA Terminal Configured Vehicle (TCV) aircraft. lithe HOS
produced eye-dwell times on various flight display systems that had a high
correspondence (r = .91) with empirical results obtained in an independent
study of actual pilots who had flown those same approaches. Although this
was the initial application of HOS to simulating complex piloting tasks,
it provides some evidence that aggregated information processing models
can also provide useful predictive data in cases where manual control and
automated systems monitoring dominate an operator's tasks.
Current Issues
The evolutions of aircraft, control and display systems, and mission
requirements are posing new problems in control: innovative aircraft con-
figurations with different dynamic characteristics and, especially, with highly
augmented controls; new types of control including six degrees of freedom
controls; and different paths to fly. These new systems are not wholly
understood, to say the least, and there have been persistent difficulties in
design, including pilot-induced oscillations, excessive pilot workload and
inadequate pilot-vehicle interfaces. There is a need both for data and for
extension of the predictive capability of pilot models to such tasks.
Because of the increasing costs associated with simulation and training
of flight control sldlls, it has become desirable to use models to assist ~
specifying simulators and In defining or monitoring training programs. In
this area, a major limitation is the lack of adequate models for the way in
which flight skills are acquired or learned.
The concern most often raised in connection with future modeling
and understanding of the pilot in the aircraft control loop is the changed
and changing nature of the pilot's taslo; owing to the introduction of
substantial amounts of automation. Thus, the roles of flight management
and supervisory control (monitoring, decision making, interacting with
intermediary computers) are becoming dominant in many pilot-vehicle
display applications. As might be expected, the data and models needed
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APPLICATIONS
55
for understanding these roles are not at all up to the standards of those for
manual flight control tasks and are clearly In need of further development.
Summary
The changing, not fully understood, nature of flight tasks, the costs
associated with aircraft development and production, as well as those
of training operational personnel,- and the history of unanticipated pilot-
vehicle interface problems arising in development—all argue for the need
for systematic, crew-centered design techniques. These techniques must
be capable of addressing the problems of the pilot (erew) in the total
system context of mission, vehicle, environment, automation, displays, etc.
Although much work remains to be done, the lessons learned in analyz-
ing manual flight control and some of Me modeling techniques that have
emerged from that endeavor can provide a sound foundation for the devel-
opment of suitable analytical and experimental methods for the problems
Of interest. Some evidence for this is given by the Procedure-Oriented
Crew (PROCRU) model (and its potential vamtions and generalizations)
discussed ~ Pew and Baron (1983) and Baron (1984~.
Aircrew Workload
Background
Crew workload and me allocation of functions to humans and machines
in aircraft have been recognized as significant and related problems at least
since the early 1950s (for example, see Fitts, 1951~. A more recent survey
(Air Force Studies Board Committee, 1982~. documents that both problems
are still with us.
Prediction of crew workload is a complex and labor-intensive task One
of the first published models developed for this purpose was based on a
task network approach (Siegel, Miehle, and Federman, 19623. It calculated
the times required for discrete operator actions from an extension of
information theory. Many subsequent estimations of worldoad for discrete
tasl~, including more recent work by Siegel and Wolf (1969), have reverted
to the use of measured or estimated task dines or task time distributions.
Exceptions include the HOS (Wherry, 1969, 1985), which was designed to
calculate task times and to predict and diagnose such workload problems as
poor d~splay/control layouts or too many allocated tasks by aggregating the
times required for microbehaviors (eye movements, information absorption,
etc.~; and Boeing's Computer-Aided Function Allocation System (CAFES),
which contained Function Allocation Modules AM-I and FAM-II) and
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56
QUANllTATIVE MODELING OF HUALAN PERFORALANCE
Siegel-Wolf type network approach Workload Assessment Modules (WAM
and SWAM).
The Vought Worldoad Simulation Program (WSP) was developed in
the early 1970s to aid in Me analysis of workload problems in carrier
landings by Navy aircraft It was later expanded to cover all phases of
flight. The WSP had separate modules for discrete and continuous control
tasks, with a scheme for blending them. As in most models of the time,
task sequences, task times, flight path tolerances, cockpit geometry, and
system configurations were all developed externally and entered the model
as inpum.
The Pilot Simulation Model (PSM) was in active use at McDonnell
Douglas from 1975 to 1978. It utilized stored data on task times to generate
worldoad estimates on discrete tasks, with particular attention to the effects
of G-load on performance.
Greening (1978) provided a critical renew of the then-known crew
workload models for aircraft operation which indicated that three aircraft
companies, Vought, McDonnell Douglas, and Boeing, were employing
different computer models to estimate crew workload. The models reviewed
were, in essence, bool~eeping models. The Greening report showed that
significant parts of the aircraft industry were using HPMs to estimate
workload. Ask time distn~utions and priorities were inputs to the models;
worldoad emerged from a comparison of task times with available tune.
As part of this working groups' effort, the three companies that reported
using workload models in 1978, plus six other airframe contractors, were
contacted to update the status of aircrew workload modeling.
Of the three airframe manufacturers who were using workload models
some years ago, two (McDonnell Douglas and Boeing) have replaced the
models, and the third (Vought) still uses the WSP model when needed but
has not exercised it for several years. The primary reason for the shift
to newer models is the rapid expansion of computer capability. The new
models are interactive with the designer and have much more capacious
and sophisticated data bases. In Me case of McDonnell Douglas, the newer
models also involve different approaches to human performance modeling,
including the OCM and operator models developed in the simulation
language SLAM.
None of the six other manufacturers contacted indicated a use of work-
load models. It seems that these companies rely wholly on human factors
expertise (including manual time line analyses) and manned simulation for
uncovering and relieving problems of workload.
During the 1970s, both HOS and CAFES were run on large main-
frame computers belonging to the Navy and were, therefore, not generally
available to outside users. Similar restrictions applied to the use of Systems
Analysis of Integrated Networks of Asks (SAINT; funded by the U.S. Air
OCR for page 57
APPLICATIONS
57
Force) Therefore, many aircrew workload problems were investigated dur-
ing the 1970s and early 1980s by human factors groups within the military,
rather Earl by airframe manufacturers. For example, HOS and WAM were
applied to the development of several emerging Navy aircraft (e.g., LAMPS
helicopter, P-3C Update, VPX, and F-18~; SAINT has been used to study
workload problems in several Air Force aircraft and over systems; and
the Army is currently investigating the use of several Apes of HPMs for
studying workload problems in its MANPRINT program.
The brief history presented here indicates that much of the funding for
HPM development, as well as the study of workload problems, has been
stimulated by the military services. Although not all airframe manufacturers
use computerized techniques for studying aircrew workload problems, the
U.S. Navy, Air Force, and Army continue to recognize and advocate the
Utica of Hems for mvestigat~g and Prolog these problems.
Current Issues
Although task analysis of aircraft missions has provided an acceptable
basis for modeling aircrew workload, a number of fundamental definition
and measurement issues have been raised over recent years. One of these
is that task-based measures are not deemed an acceptable definition of
workload by some researchers and users. Some investigators feel that a
clean distinction should be made between human operator perfonnance re-
quirements, such as result Dom task analysis, and human operator mental
effort expended (i.e., a trained operator might perform a task with time and
cognitive resources left over, whereas a novice may be fully occupied). They
emphasize that the human mental effort expended (not physical calonme-
try, which is largely irrelevant) in psychomotor skills or cognitive tasks
is important, and if an individual human-centered measure (performance
and effort expended) could be found, it might become a more sensitive
predictor of human limitation and system failure than either a task-based
measure or a system performance measure.
One performance related measure occasional used is secondary task
performance, which helps assess how well the operator can do an artificially
imposed task added to the pnma~y task. However, this measure is often
deemed unacceptable by pilots and others because it interferes with the
primary task. Many physiological measures of workload have been tried,
but all exhibit significant measurement noise and require many seconds or
even minutes of data to establish a single workload data point. Probably
the most acceptable mental effort measurement technique is the subjective
rating scale now employed by Airbus Industries and the U.S. Air Force.
Recent research has sought to determine whether psychomotor busi-
ness, emotional stress, and pure cognition can be measured separately, and
OCR for page 58
58
QUALITATIVE MODELING OF HUMAN PERFORMANCE
whether the components are additive in determining total subjective mental
worldoad.
Summaly
Risk analyses have yielded models for pilot workload in terms of
percentage of hypothetically available time required by sensing, motor,
and cognitive activities. Recent efforts have sought to measure and model
mental workload.
Air-to-Surface Search and Targeting
Background
The problem of finding objects on the earth's surface from a moving
aircraft has been recognized since the early days of flight. One of the
earliest models of the air-to-surface search process was published as part
of a study of the especially difficult regime of nap-of-the-earth flight (Ryll,
1962~. This and many other early modeling efforts were summarized by
Greening (1976~.
As new sensors were added to aircraft equipment, the search and tar-
geting activity became more distinct from piloting and was often performed
by a separate crew member. A number of models for the use of quasivi-
sual sensors television (TV) and Forward Looking Infra Red (FLIR) were
summarized in a report by General Research Corporation for the Naval
Weapons Center (Stathacopoulos and Gilmore, 1976~.
The HOS model was used as the basis for an Operator Interface Cost
Effectiveness Analysis (OICEA) by Lane et al. (1979) to examine the effect
of proposed additions of FLIR-related tasks to an electronic countermea-
sures (ECM) sensor operators job in a Navy P-3C aircraft. 1b provide
comparative data, three versions of the aircraft were simulated: the base-
line version without FLIR equipment or tasks, the prototype version that
had added (but not integrated) FAIR equipment and tasks, and a proposed
Update version with more integrated and automated FLIR equipment and
tasks. Comparison of HOS simulation results for the baseline and prototype
versions confirmed actual fleet results which had shown that performance
of the normal ECM tasks would be significanth,r degraded and performance
of the FL-JR tasks would rarely be successfully completed in the proto-
Wpe aircraft. However, the study also showed that the Update version
would permit all of the FI~IR-related tasks to be successfully completed
and performance on the ECM tasks to be enhanced.
OCR for page 59
APPLICATIONS
Current Issues
59
The multiple-sensor aircraft poses problems of a spinal sort, especially
when used in high-intensity convict where line~f-sight exposure to the
target area may be dangerous, and an active sensor such as radar can be used
only briefly and intermittently. The targeting Bunion process then becomes
one involving difficult trade-ofl\ between the nslo; associated with search
and the need for current target data. Nonimage data (such as flight vectors
or coordinates) must be blended win the output of automatic classifiers and
with intermittent imagery in the most efficient way. A modeling approach
to this problem is being developed at the Naval Weapons Center and
elsewhere (Greening, 1986~.
Summary
Numerous target acquisition models have been developed and used
over the past 25 years. However, the bunk of model development and
validation work is becoming obsolete because of changes in tactics, the
proliferation of sensors, and advances in sensor technology including a
variety of automatic targeting systems.
Because of the substantial Log in modeling relative to advances ~ tech-
nology and changes in tactics, models have not, in general, had substantial
impact on the development of new or improved sensors. Their utility has
been greater in tactical plannung and related, postdesign acquires.
The most active air-to-surface sensor modeling areas currently are
those directed toward (1) enlarging the scope to include more of the
relevant context and (2) keeping up with developments in sensor technology.
lIUMAN PERFORMANCE MOOEI5
IN NUCLEAR POWER OPERATIONS
Background
The number of human performance modeling simulations actually
applied within the nuclear industry is at present very small. Although
considerable theoretical work has been done (e.g., Shendan, Jenkins, and
Kisner, 1982), translation of that work into everyday plant operations
has been limited. Other than instances in which cognitive modeling has
been incorporated into operator aids (e.g., West~nghouse's DICON work;
U.S. Department of Energy, 1983), the majont~r of applications have been
associated with risk assessment and nuclear power plant safety (U.S. Nu-
clear Regulatory Commission, 1982~. Most of these cases have involved
responses to requirements of the Nuclear Regulatory Commission.
l
OCR for page 60
60
QUANTITATIVE MODELING OF HUMAN PERFORMANCE
In a recent meeting, nuclear experts discussed the capabilities of
methodologies currently used for risk assessment. That meeting provided
useful insights into the status of human performance modeling as well as
the reasons behind that status. It became evident that human performance
modeling should not be considered as isolated from other techniques be-
cause actual plant usage was the result of many implicit decisions about
strengths and weaknesses in available methods. Consequently, this discus-
sion considers HPMs within a framework of the available technology.
There are five main techniques used for the assessment of human
related risks: Unique for Human Error Prediction (THERP), Oper-
ator Action Tees (OATS), Maintenance Performance Prediction System
(AL9PPS), Sociotechnical Approach for Human Reliability (STAHR), and
SLIMIMAUD (Success Likelihood Index Methodology/Multiattribute Util-
ity Decision). Of the five, only MAPPS utilizes discrete task network
simulation as the sole basis for prediction. Why the large body of theoreti-
cal models that exists has not been utilized more completely is best seen by
a relative comparison of the strengths and weaknesses of other techniques.
A brief summary of the methods follows.
The THERP technique (Swain and Guttman, 1980) is probably the
oldest and most established human reliability assessment method and was
originally developed by Swain (19643 at Sandia National Laboratories for
military applications. The method relies on task decomposition into micro-
scopic actions via a highly detailed task analysis. This analysis breaks down
operator behavior to a level of indMdua1 actions such as reading a graph,
reading an instrument, or turning a control knob. Each series of operations
is described by a probability tree composed of sequential actions in which
the probability of an action at any branch is drawn from tables. In a few
cases these probabilities are based upon objective evaluations, but in most
cases subjective expert opinion is used.
The OATS also utilizes probability trees to structure operator actions
but has much larger units of analysis, usually plant functions rather than
operator tasks (U.S. Nuclear Regulatory Commission, 1984~. A probability
is placed on each [unction, based on the time that would be available
to perform the function within particular scenarios. AS a result, heavy
use is made of time/reliabili~ curves relating probability of performance
to available tune. Times are computed based on the time required to
recognize and diagnose a plant condition. Each time is defined as total
time available minus He time required to execute an operator action.
Currently, OATS uses three types of curves to provide a human reliability
value; each is based on the nature of the operator action.
The MAPPS system is a discrete event simulation (Siegel, Bartter,
Wolf, and Knee, 1984~. In its current form, it addresses only maintenance
behavior. It is menu driven and includes a variety of parameters whose
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APPLICATIONS
61
OCR for page 62
62
QUANTITATIVE MODELING OF HUMAN PERFORMANCE
it appears better than existing methods for maintenance analysis, possibly
because the over approaches are oriented toward operators.
Analysis of the other methods' strengths suggests the following. Fast,
HPMs may require more parametric data than are usually available in
actual industrial settings. Second, labor-intensive techniques can provide
subtle decision rationales that may be lost in stochastic methods such as
Monte Carlo simulations. Third, expert group techniques provide greater
flexibility for considering situation-dependent tasks. Finally, dimensions of
plant cooperation and ease of use weigh heavily in applications. Human
simulation methods currently do not have an effective interface to normal
plant user environments.
A comparison of where the approaches are deficient provides addi-
. -
tional insights. Regarding THERP, its strengths are that traceability of final
event probabilities to original situations is good. Flexibility is high because
it can deal with unusual tasks. It is weak in that it requires what many
consider to be an inordinate amount of training, it is extremely resource
intensive because it requires task analysis for every task, and it can be very
vulnerable to misuse or biasing if not used as prescribed. The latter results
from a tendency of users to skip to probability tables and bypass important
intermediate steps.
For the OATS approach, traceability is also good because only one
variable is involved. Reproducibility (i.e., interrater reliability) is good,
and there is a low requirement for Gaining, which is largely due to the
somewhat simplistic nature of the method. This approach tends to be
inflexible because it can be used only for certain events, and it is very low
in completeness because it operates at too general a level of analysis to
encompass the full range of probabilistic risk assessment problems.
In the MAPPS simulation, reproducibility, but not traceability, is high
because MAPPS uses stochastic branching. Compared to the analysis level
of THERP, the resources required are minimal The model is strong on
completeness because the erects of variables have been quantified carefully
and are drawn from a systematic analysis of years of research into factors
important for maintenance performance. In terms c)f weaknesses, MAPPS
currently deals only with maintenance; it is also weak in its ability to handle
unique task factors.
The STAHR approach is strong in the last area. It can be readjusted
quickly by changing influence diagrams; it has good traceability because
the reasons for using each value are documented, the group procedures
reduce individual biases, and extended discussion of plant characteristics
and actions pennits great specificity of task definition. Weaknesses are
similar to THERP in that training is needed tO permit groups to work
effectively together, and it is both resource and time intensive. In contrast
to THERP, the resources are people rather than data.
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APPLICATIONS
63
lithe SLIMIMAUD approach is high on traceability, flenbili~, and
specificity of task definition. One of its principle weaknesses is that the
structure appears to preclude the evaluation of performance variable inter-
actions.
1b draw conclusions, it appears that the greatest gains for the in-
dustry may come from using human performance models as a part of a
hybrid technique rather than in a stand-alone mode. Because the above
approaches do not all operate at the same content level, analysis may best
be made through combinations of techniques rather than a single approach.
Current Issues
Within the domain of plant safety, four issues currently appear to
be the most important. The first is how analysis can best be applied to
cognitive tasks, particulars in such areas as confusion between competing
symptoms of plant events. Such questions have been studied by using
confusion matrices that have symptoms on one axis and plant events on the
other.
Additional issues concern identification of those human variables that
are really important in plant performance. What constitutes a satisfactory
cognitive model of the operator, how the costs of HPMs can be compared to
their benefits, how potential users should be acquainted with the technology
available, and how human and power plant hardware models can best be
integrated are all examples.
Validation is clearly the most important current issue. It manifests
itself in three ways: data collection problems including the acceptability
of hardware simulator data and the difficulty of field data collection; the
interpretation and reduction of collected data; and the comparison of
potential approaches. The most fundamental mtenon is how well a model
works in the field. lb answer that question, better data are needed.
Because obtaining data is difficult, the use of human performance modeling
techniques is slow, particularly for rare accident events.
A second area concerns issue selection. The questions involved are
whether the selected performance variables are correct ones and how the
nuclear Industry can be certain they are.
A third area concerns the ability of models to deal with events outside
the realm of the expected because rare accident events are central to plant
safebr.
A fourth area is misdiagnosis behavior and how it can best be ad-
dressed. This area may or may not become less important because of
recent emphasis on symptom-based (i.e., unknown cause of abnormality)
diagnostic procedures instead of event-based (ie., known cause of abnor-
mality) procedures.
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64
QUANTITATIVE MODELdNG OF HUMAN PACE
A fifth area is the previously mentioned question about coupling of
methodologies. Specifically, can human simulations effective couple to
already existing techniques such as THERP or SLIM/MAUD? Another area
is the use of human operator models in design specification, particularly for
purposes of increasing human reliability. The final area concerns what can
be done to eliminate confusion and increase correct diagnosis probabilities,
given the occurrence of a misdiagnosed event.
Summary
This section has examined human performance modeling for the nu-
clear industry from a particular perspective, namely, human reliability and
risk assessment That perspective was adopted for two reasons. First it
depicts the way in which it Is actually applied in industry. Second, insights
into why models are and are not used were discussed by comparing an
existing model (MAPPS) with the limited set of methods currently used for
risk assessment. By considering other approaches, it was possible to place
a human performance model into the perspective of an entire technology
area. This has often been difficult in many broad-based technology areas
such as military applications. As a result, direct comparisons of strengths
and weaknesses could be made to highlight not only what role the methods
serve, but also to identify more directly what Uade-offs had been made
among recurrent questions such as ease of use, resource requirements,
specificity of analysis, reliability, and traceability. As mentioned at the be-
ginning, the actual use of human-related models in the nuclear industry is
extremely limited. The models applied appear to be the result of a practical
mix of many of the factors described above. The extent of future model
usage win probably hinge more on the result of changes in available data
and resource support than on He actual state of human model technology.
HUMAN PERFORMANCE MODELS
IN MAINTENANCE OPERATIONS
Background
~la~ntenance Is different from many of the other tasks discussed in
this report. In particular, although time is an important attribute (i.e., the
sooner something is repaired, the better), system maintenance is usually a
static task because the system state does not change without human input.
Of equal importance, maintenance can be a very complex task when un-
expected and unfamiliar faDures occur. In such situations, problem-solving
skills are central and psychomotor skills are of secondary }mportance.
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: -
APP~CATIONS
65
This section briefly reviews HPMs for predicting maintenance per-
formance. One model, MAPPS, has been discussed in the context of
nuclear power operations. For the purposes of this review, maintenance
performance Is characterized at three levels:
1. action-by-action sequences of observations, tests, and repairs (re-
ferred to as SEQUENCES);
2. overall times and errors associated with particular sequences (re-
ferred to as TIME/ERRORS); and
3. mean time to repair and probability of error across sequences or
equipment systems (referred to as M=R/PERR).
The maintenance models discussed here produce outputs in one or
more of the above levels. Inputs to these models include one or more of
the following:
1. representations of the equipment, either physically or functionally;
2. representations of the maintainer in terms of
3.
· general characteristics (e.g., parameter variations),
action selection criteria (e.g., maximum information gain or
minimum time),
· knowledge and skills (e.g., understanding of equipment); and
results of task analyses (i.e., maintenance SEQUENCES).
Based on the above characte~tions of outputs and inputs, six repre-
sentative maintenance models are summarized in Bible 3-~. It is interesting
to note that the approaches underlying these six models (second column
of Bible 3-1) represent the full range of modeling approaches discussed in
this report. Thus, there is no one-tone mapping from application domain
to appropnate modeling methodologies.
In distinguishing among the models in Able 3-1, Wohl's (1982) model
and that of Siegel et al. (1984) emphasize global performance measures
such as M,l~lK. Traditional labor-intensive maintainability analyses have
a similar focus (Goldman and Slattery, 1964~. In contrast, He models
of Hunt and Rouse (1984) and of 1bwne, Johnson, and Convin (1982)
emphasize fine-grained predictions of SEQUENCES. The model of Madni,
Chu, Purcell, and Brenner (1984) falls on the global side of these fine-
grained approaches. Therefore, the choice among the models in Able 3-1
depends on the level of performance to be modeled.
Summary
It would seem feasible to use fine-grained models to produce the SE-
QUENCES to meet the task analysis requirements of the global models.
This approach would reduce the analytic effort required and produce perfor-
mance predictions at all levels. However, the knowledge-engineering effort
OCR for page 66
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APPLICATIONS
67
required to undertake this (relative to both equipment and maintainer) is
probably unpractical when the modest levels of investment normally made
in maintainability analyses, which are sometimes viewed as a necessary evil,
are considered.
HUMAN PERFORMANCE MODELS
IN SUPERVISORY CONTROL
Background
Supervisory control is an example of an important merging class of
human operator activity in which HPMs are needed but for which proven
models do not now Ernst. Simply stated, supervisory control refers to all
the activities of the human superior who interacts via a computer with a
complex semiautomatic process. It can substitute for direct manual control
of vehicles or plants.
The term supervisory control is derived from the close analogy between
the characteristics of a supervisor's interaction with subordinate human staff
members and interaction with automated subsystems. A superior of peo-
ple gives general directives that are understood and translated into detailed
actions by staff members. In turn, staff members aggregate and transform
detailed information about process results into summary form for the su-
pe~or. The degree of intelligence of staff members determines the level
of involvement of their supervisor in the process. Automated subsystems
permit the same type of interaction to occur between a human supervisor
and the process Darrell and Sheridan, 1967~. As indicated in another re-
port of the Committee on Human Factors (Shendan and Hennessy, 1984),
supe~ory control behavior is interpreted to apply broadly to vehicle con-
trol (aircraft and spacecraft, ships, undersea vehicles), continuous process
control (oil, chemicals, power generation), and robots and discrete task
machines (manufacturing, space, undersea mining).
In the strictest sense, the term supervisory control indicates that one or
more human operators set initial conditions for, intermittently adjust, and
receive information from a computer that closes a control loop through ex-
ternal sensors, effecters, and He task environment, as illustrated in Figure
3-1. Typically, supervisory control involves a Sve-step cycle of the superv}-
sor's activity (Sheridan, 1986), which includes the following functions:
1. planning what to instruct the computer to control automatically,
which involves the supervisor in (a) coming to understand the nature of the
controlled process, inputs, and other physical constraints, (b) deciding on
the tradeoffs between various benefits and costs, and (c) thinking through
a strategy for arranging the task;
OCR for page 68
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APPLICATIONS
69
2. instructing or actually programming plans into the computer to do
(or start to do) certain things automatically for normal operation or to stop
some actions when they are complete or abnonnal;
3. monitoring, that is, (a) allocating attention among many sources of
information about what is going on, including direct sensors, biocomputer
knowledge bases and expert advisory systems, documents and human ex-
perts? or others in order to watch the (usually normal) automatic operation
of the system, and (b) estimating the current system state and deciding if
it is satisfactory, or if not, to diagnose what has gone wrong;
4. intervenm&, that is, breaking into the automatic control loop either
in a minor way to adjust set points of automatic control or in a major
way to stop one task and start a new one, to take emergency actions
(fault management) manually, or for maintenance or repair; this involves
reprogramming (loop back to step 2~; and
5. Iear7zut,g that is, acquiring from experience what is necessary for
better future planning (loop back to step 1) or other supervisory functions.
Each of these [unctions and subfunctions may be said to involve a
separate mental model, though the term as used today Is mostly restricted
to step leaf. Each may be augmented by a computerized decision aid of
some type, in addition to the computerized automatic control.
Although a variety of models of supervisory control have been pro-
posed, including the PROCRU model discussed earlier, there is little
consensus on which way to proceed. One of the major problems in model-
ing human supervisory control is that formulation of the objective function
is an active role of the supervisor; it is not given a priori. There are usually
as many objective functions as there are people or occasions where one
objective has different strategies. None of these is easily specifiable in other
than fuzzy linguistic terms.
A model can be (1) a paper description of a system, i.e., a theory.
Alternatively, it can be (2) a functional model implemented on a computer,
which emulates the function of a system, or (3) a mental model, an internal
representation of a system held in the mind of an operator, designer, or
researcher. Supervisory control is particularly complex because multiple
elements of (2) and (3) must be combined into a single physical system
which must, in turn, be combined with (1~.
From Figure 3-1, it is clear that a model of supervisory control must
be a model of the entire system, not just the human. The situation is
similar to that found in models of the human operator in manual control
systems, where the particular realization of the model depends in each case
on the properties of the rest of the system. Humans modify their behavior
to compensate for, or complement, other elements of the system; for that
reason, all of them must be represented.
OCR for page 70
70
QUANTITATIVE MODELING OF HUMAN PERFORMANCE
A supervisory control system is one in which there Is little or no overt
human activity for considerable periods. The tasks that a human must
early out are initiated by plant states or by operators receiving goals from
a higher authority such as management. Each special function requires
a model, and a model of the supervisory controller would describe the
interaction of human and computer as a function of plant state.
Several models of limited scope may be relevant to supervisory control.
Moray (1986) reviews some 10 models of monitoring. There are many
models of decision making and several models of fault detection for a varieW
of different tasks (Rouse, 1983; Moray, 1986; Wohl, 1982~. Intervention
to trim the system set point could probably be modeled by a conventional
expert system. However, none of these models supervisory control. If other
models of planning, monitonng, and fault management existed, they might
be used to predict behavior in supervisory control, provided that the state
of displayed information was also known. For example, if the operator
had recently looked at variables LYE, Y2, ..., Ye, ..., Yn) and those values
were known, a model might suggest that the operator would recognize that
the system was in state Si, and that, by using a production system, one
might predict planing or action Pj, Hi from S.' and a knowledge of the
operator's goals.
A unique feature of supervisory control is the passing of control
backward and forward between operator and computer (see, for example,
Sheridan and Verplank, 1978~. There is an ability of the system to make
judgments on the bash of its knowledge and to enter into a dialogue
with me human operator. There have been a few attempts to provide
solutions for the allocation problem, such as those of Sheridan (1970) and
Moray, Sanderson, Sluff, Jaclo;on, Kennedy, and Ting (1982), but these
are algorithms (comprehensive procedures for obtairung a desired result)
rather than models of human performance.
The situation is reminiscent of Simon's (1981) attitude toward human
behavior:
A man viewed as a behaving system, is quite simple. The apparent
complexity of his behavior over time- is largely a reflection of the
complexity in which he finds himself.... We can often predict behavior
from knowledge of the system's goals and its outer environment, with
only minimal assumptions about the inner environment.
In this regard? Sanderson (1985) notes that
It is obvious that the sort of goals being pursued in basic cognitive
research and those being pursued in applied cognitive engineering are
very different.... The questions being posed in basic research are often
conceptually sweeping and are ideally task-free.... Ike concern is that
... fundamental principles ... emerge.... In an applied cognitive setting
OCR for page 71
APP' [CATIONS
however the task takes precedence.... When trying to understand, say,
how an expert does a task a great deal of the researchers' time and effort
goes into understanding the task itself. The model of human behavior
which emerges has more reference to the task than is normal, or even
considered proper, for basic research.
· . .
71
This may be a good point of departure for developing a model of
supenriso~y control. Because the human plays a quantitatively slight but
qualitatively important role in such systems, a model of the machine is
as important as one of the human. Of the existing models, PROCRU
is a good start because the expert system portion of it allows planning,
reasoning, and procedure choice to be modeled.
Summary
Supervisory control is an emergent class of systems wherein humans su-
pervise computers and computers perform the direct control. It poses new
demands for integrated human performance modeling, inherently demand-
ing component models of high-level activities such as planning, teaching,
monitoring, failure detection/inte~vention, and learning. It also poses a new
perspective with respect to dependence on both the task and the initiative
of the human operator.
Representative terms from entire chapter:
supervisory control
