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OCR for page 176
LIVE FACIt:)RS ~ TO DESIGN AND DEVE=~T
OF SOlIW~ IN THE SPACE STATION
Peter G. Polson
Achievement of the cperational arxt productivity goals for the Space
Station will rewire expensive use of a wide varied of ~uJcer-bas~
systens ranging freon application programs that Nan on gene al pus ~ se
work stations to specialized embedded computer systems that monitor,
operate, and trouble shoot critical subsyst Is, e.g., environmental and
pawn' control systems (Anderson and Chambers, 1985; Johnson et al.,
1985). However, improperly designed user interfaces for these systems
will compromise these goals.
The objectives of this chapter are to characterize major problems
involved in the design of human-computer mterfa~=c for systems on the
Space Station and show how systematic application of empirical and
theoretical results and methodologies from cognitive psychology and
cognitive science can lead to the development of ~nterfa~=c that reJu~-
training cost and enhance space station crew productivity. This
chapter focuses on four issues: 1) transfer of user skills, 2)
comprehension of complex visual displays 3) human-computer problem
solving, 4) management of the development of Ale systems.
~BLEMS
Transfer of User Skills
Inconsistent user interfaces in which the same basic function is
performed by several methods In different contexts resume= transfer and
interferes with retention (Poison, 1987; Postman, 1971~. m e Spare
Station's numerous computer-based systems and applications programs
will be developed by different organizations over a period of many
years. Inconsistency will be the rule rather than the exception unions
extrao ~ y measurer are taken in the design of user-~nterfaces for
these systems. Popular and pcwerfu~ applications programs developed
for personal computers could be realistic models for software developed
for the Space Station.
The typing popular applications program for a personal computer has
been developed by an independent organization; the program has a great
deal of functionality which is the reason for its commercial success.
176
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177
m e user interface is unique to the application being embedded in the
application's code. Effective use of the application requires
specialized training and several weeks of experience. There is no
consistency across different popular applications. For example, they
can have very different methods for editing operations on a text
string. Emus, editing an axis label on a graph, editing an operating
system command, or modifying a line of text with an editor all require
different sequence= of user actions.
The Comprehension of Complex Visual Displays
Complex visual displays using graphics, color, and possibly motion will
be used in the space station to present various kinds of information to
crew members carrying out complex tasks. Poorly formatted, poorly
organized, arx] difficult to comprehend displays will have negative
impacts on the pr~uctivi~r. Such displays increase training costs,
difficulty of ccrnplex tasks, anti probability of serious operator
errors.
mere exists extensive knowledge of the processes involved In the
perception of basic visual properties like color and form (Gus ham,
1965; Walraven, 1985), and there are numerous guidelines for display
layouts and use of symbols and color (e.g. Smith and Moser, 1984;
Kosslyn, 1985). However, there is no systematic knowledge of how
people comprehend complex displays or use the information presented in
such displays to perform complex tasks. There are no general
principles for the development of effective complex displays.
Human-Cc mputer Pro bleat Solving
Nabs ha= extremely ambitious plans for the use of artificial
intelligence and robotics in the space station. The proposed
application areas include information management, life support systems
operations and monitoring, electrical power systems operations and
monitoring, and guidance and navigation. Many of these tasks on the
Space Station will be performed by systems with significant embedded
intelligence in order to satisfy mission, technological, and economic
constra Mets and to achieve productivity goals (Anderson and Chambers,
1985).
The a-=- of artificial intelligence techniques can significantly
increase the complexity of a system from the point of view of its human
user. The crew member must now understand both the task performed by
the system as well as the Characteristics of Jche "intelligent" control
prearm (Hayes, 1987~. Waterman (1986) notes that expert systems are
"brittle" Men pushed beyorxt the very narrow ocean of their gal
expertise can fail with little or no warns. Uncritical use of the
current state of-th~t in eat systems' technology Mule decrease
productivity of the crew and Manger their safety. Achievement of
NASA's plans for the applicatior~s of artificial intelligence in the
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178
Apace station will rewire extensive basic research arxi rapid advances
e state~f-the-art.
SOLUTIONS
Four solutions are propose for the problems outlined in the pry
sections: 1) Use of information pressing meets of tasks In the
design pa ~ ss, 2) allocation of ad ~ ate resour ~ to user-interface
development, 3) use of user interface management systems, and 4) use
of existing expertise On NASA.
Detailed Information-processing Models
The first, an] most important, solution is that designs for
applications programs, complex visual displays, and cooperative
human-oomputer problem solving systems be based on detailed,
information-processing models of cognitive processes involved ~ the
performance of specific tacks. Information-processing models describe
the knowledge, cognitive operation_, and user action_ r ~ ed to
_~_m ~ - _~1' The== m~01 C ~~ - =1 Cal he 11C~ - ^ - ~~_~= - I; I; One
~LLvLlll ~ J. 111~= Il~=l~ ~l ~~ ~ ~ ~v- My ~t ~ ~ ~ - - ~ Van
of usability parameters' e.g. training time, productivity, and ment=1
work load, and they can be used to isolate design flaw_ An proposed
versions of a cc~nputer-based system.
Information-processing Myers describe that ~ ransfers, the knowledge
necessary to perform the task, and thus they can be used in ye design
of consistent user interfaces bat facilitate transfer of user skills.
Information~pr~xessing Myers can make important con~cributions to the
develop ~ nt of effective ~ plex visual displays. The models describe
both the knowledge necessary to successfully complete a task, what is
to be displayed, and the processes involved in extracting that
knowledge from displays, how it is to be displayed.
~nformation-processing models are an important component In the
successful development of effective human-oomputer problem solving
systems. There is general agreement that successful human-oomputer
problem solving systems will incorporate models of the task and the
user (Hayes, 1987~. Current theoretical methodologies in cognitive
psychology and cognitive science can be used to develop both kinds of
models.
Management of the Design Process
The second solution involves suocessfu' management of the development
process for computer-based systems. +~^ ~ ~:~_, ~.~1~ ~~- ~ ~ ~~=
~ lle ~ )~-L~L ~GVCl~J—llCll~ ,~' Vie>=
for complex camputer-based systems In the military, NASA, end the
civilian sector does not allocate enough resources to ~~hili~
considerations. The primary focus of the process is on developing a
system with specified functionality. h~nctiona~ ity is necessary but
not sufficient for usability. Usability, training time ark]
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179
productivity, is typically evaluated late in the design cycle when itis
far too late to make changes that improve 1l-c~hiligy. The design of
highly productive complex camputer-based systems requires solving
simultaneously two interrelated sets of design problems involving
functionality and usability.
What is proposed in this chapter is that ~~.~ hility and functionality
considerations receive equal weight during all phases of the design
cycle. The preliminary version of the system is evaluated for
ility. If the system fails to meet usability goals, the design is
revised. The revise] design is then evaluated. This iterative process
continues until the design meets both usability and functionality goals
(Gould and Lewis, 1985; Hayes, 1987~.
User Interface Management Systems
The third solution involves the use of appropriate technologies. Many
of the problems involving transfer of user skills and consistency
across applications can be solved using user interface management
systems. m e nature of these systems is discussed in Hayes (1987) and
Hayes, Szekely, and Turner (1985). They will not be discussed further
here.
Existing Expertise in NASA
The fourth solution involves making effective use of the expertise
already within NASA. What is being pro posed here is similar to other
modeling efforts currently underway in NASA deal ing with problems of
anthropometrics and habitability. OPSI~ (Glabus and Jacoby, 1986) is a
computer model that simLlat~c crew actions and interactions carrying
out specific tasks under constraints imposed by different interior
configurations, crew size and skills and other environmental factors.
m ese simulated task scenarios are used to rapidly explore a large
number of variables involving the environment and crew composition
iteratively develc ping a more optimal design. Detailed models of the
cognitive Operations and physical actions required to carry out various
types of tasks involving interaction between man and machine can be
used in a similar fashion to aptimize designs for user interfaces.
Alternative Solutions
Guidelines and Handbooks
Human factors guidelines (Smith and Mbsier, 1986) and handbooks
summarize information ranging from design goals and methodology to
specific data on perceptual and Sector processes. Guidelines and
handbooks contain parametric information about basic perceptual and
actor processes and information on limitations of classes of
interaction techniques. However, they are of limited use in
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180
characterizing higher-level cognitive processes, e.g. comprehension,
learning, and problem solving. Guidelines propose reasonable design
goals for cognitive aspects of a system, but they contain little or no
advice on how to achieve such goals. Examples of ccgn~tive guidelines
include '~Ldn~mize working memory load" and '~Lntmize the amount of
information the user Hal to memorize".
Utility parameters characterize the use of a system to perform a
task, e.g. training time, productivity, and user satisfaction.
Develc ping a system that optimizes ~~C~hility parameters requires
understanding of the task and the cognitive processes involved An
performing the task. Mast features incorporated into user interfaces
are not good or bad per sa. Usability is determined by interactions of
the specific features of a design with the structure of a bask.
Guidelines do net contain necessary information about task structure,
the knowledge required to perform a dark, or the dynamic-= of the
cognitive processing required to perform the took. Our knowledge of
cognitive processes is in the form of detailed information processing
models of the performance of complex tacks.
Many writers (e.g. Gould and Lewis, 1985; Hayes, 1987) argue that
successful interface design is an iterative process. This view is
strongly championed In this chapter. It is not possible to derive an
optimal interface from first principles. Accumulated experience,
information in guidelines and handbooks, and careful th~n~-etical
analyses can lead to the develcpment of a reasonable initial trial
design. However, this design has to be evaluated, modified, and
evaluate] again. In other words, guidelines and handbooks are not
enough.
Empirically BACH Modeling Strategies
Gould and Lewis (1985) and Carroll and Campbell (in press) seriously
question the theoretically driven design and evaluation processes
championed in this chapter. They argue that there are serious
limitations of current modeling techniques, e.g. the limitations on cur
knowledge of comprehension of complex visual displays. They champion
empiri~lly-based modelling and evaluations methodologies. Many
successful, complex systems, e.g. today's generation of highly
automated aircraft, evolved from a combination of increasing technical
capabilities, e.g. highly reliable microprocessors, and expensive
operational experience (Chambers and Nagel, 1985).
However, relying on empirical methods to evaluate trial designs has
serious limitations. m ey include difficulties in extrapolating
results, doing experiments to evaluate complex systems, and evaluating
transfer of training. For example, in a very complicated system, it
may not be feasible to do empirical studies to evaluate a large number
of tasks or to evaluate transfer between many tasks. If the current
version of a trial design has unacceptable usability parameters, a
designer has the very difficult Kayak of deciding what attributes of the
current design should be changed in order to improve performance. A
theoretic=] model provides an explicit decomposition of the complex
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181
underlying processes. This a~(litional detail describing the underlying
pro can be very valuable In making well motivated changes leading
to Me next iteration of the design process.
OUTLINE OF AIDER OF COPIER
The remainder of this chapter is organized into five sections. The
first provides a general characterization of the kinds of theoretical
Gels of cognitive processes ~t we argue should be the - his for the
design of highly Ale Outing sys~cerns. The nest section describes
a detailed analysts of ache process involved inches transfer of user
skills and presents s=ries of empirical results supporting these
theoretical analyses. This section also praises a description of
current theoretical Models of human~uter interaction. Transfer is
a well understood problem. - ~ ~ ~ ~ ~
. . .
lhe objective of this lord secc~on Is No
provide an ~~trat~on or a su~r==sful solution. The next section
describes some of the difficult problems involved in the design of
effective complex visual displays. me fourth section discusses the
problems involved in the develcpment of effective cooperative
manrrachine systems. The final section mates recc=mendations for
further research.
MODISTE OF ~ EXCESSES
the information processing framework (Newell and Simon, 1972; Gardner,
1985) provides the basis for the develcpment of detailed process models
of tasks performed on the Space Station. These theoretical analyses
can be `:=P~ as the basis for the design of human-computer 1nterfa~=
that have minimal training costs and for the task and user models
incorporated into human-co mput~' problem solving systems.
The Information Prccessing Framework
An information processing model ~ncorpo~= representations of the
task, the knowledge required to perform the task, and the processes
that operate on the representation to perform the bask (Gardner,
1985~. Such models are often formalized as computer simulation
programs. The framework characterizes the general architecture of the
human information processing system which in turn constrains the nature
of the representations and the processes that operate on them, e.g.,
limited immediate memory. Newell and Simon (1972) and Anderson (1976,
1983) have pro posed that the human information processing system can be
describe] as a production system. The following section describes
production system models of human-computer interaction.
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182
M~els of Wean Outer Interaction
The GO Feed (Card et al., 1983) and Cognitive C~lexi~r merry
(ccr) (Kieras arc] Poison, 1985) both characterize the knowledge
necessary ~ mace effective, routine use of software Cools like an
Operating system, a text editor, or a data-base manager. me GCm
formalism describes the content and structure of the knowledge
underlying these skills. ccr represents this knowledge as production
rules which permits one to quantify amount. CCT 1nc~-rporat~c all of
the assumptions of the GoMS model. m e production rule formalism
enables one to derive quantitative predictions of training time,
transfer of user skills, and performance. m e next two sections
describe each framework.
lhe Gem; Model
The GONE mated represents a user's knowledge of how to carry out
rout We skills in terms of goals, cremations, methods, and selection
nlles.
Goals represent a user's intention to perform a task, a subtask, or
a single cognitive or a physical operation. Goals are organized into
structures of interrelated goals that sequence cognitive operations and
user actions.
Operations characterize elementary physical actions (e.g., pressing
a function key or typing a string of characters), and cognitive
operations not analyzed by the theory (e.g., perceptual operations,
retrieving an item frum memory, or rending a parameter and storing it
In working memory).
A user's knowledge is organized into methods which are subroutines.
Methods goner ate sequences of operations that accomplish specific goals
or subgoals. me goal structure of a method characterizes its internal
organization an] control structure.
Selection rules specify the conditions under which it is appropriate
to execute a method to effectively accomplish a specific goal On a
given context. m ey are compiled pieces of problem solving knowledge.
They function by asserting the goal to execute a given method ~ the
appropriate context.
Content and Structure of a User's Knowledge
The God; model assess that execution of a task involves decomposition
of ache task into a series of SUbt;`=kS. A skilled user has effective
methods for each type of subta~k. Accomplishing a task involves
executing the series of specialized methods that perform each subtask.
There are several kinds of methods. High-level methods decompose the
initial task into a sequence of subtasks. Intermediate-level methods
describe the sequence of functions necessary to complete a subta.ck.
Low-level methods generate the actual sequence of user actions
n~r~=sary to perform a function.
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183
A user's kna~riedge is a mixhwe of task-specific information, the
high-level methods, art sys~cern-s~cific Repledge, the law-le~rel
methods. The kna~riedge captured In the God; representation describes
both general knmrJecige of how the task is to be deco Deposed as well as
specific information on how to execute functions require J to complete
the bask on a given system.
Cognitive Complexity meory
Xieras and Polson (1985) propose that the knowledge represented in a
GEMS model be formalized as a production system. Selection of
production systems as a vehicle for formalizing this knowledge was
theoretically motivated. Newell and Simon (1972) argue that the
architecture of the human information pressing system can be
characterized as a production system. Since then, production system
models have been developed for various cognitive proposes (problem
solving: Simon, 1975; ~rat, 1983; text c~r~hension, Kier~s, 1982;
cognitive skills: Anderson, 1982~.
An Overview of Production System M~els
A production system represents the knowledge necessary to perform a
back as a collection of rules. A rule is a condition-action pair of
the form
IF (condition) THEN (action)
where the condition an] action are both complex. The condition
represents a pattern of information in working memory that specifies
when a physical action or cognitive operation represented in the action
should be executed. The condition includes a description of an
explicit pattern of goals and subgoals, the state of the environment,
(e.g., prompts and other information on a OK display), and other
needed information in working memory.
Production Rules and the GoMS MbJel
A production system model is derived by first performing a GOMS
analyses and then writing a program implementing the methods and
control structures described in the GCMS model. Although GCM5 models
are better structural and qualitative description of the ~c~riedge
nectary to perform tasks, expressing the kn~riedge and processes
he production system formation permits the derivation of well
motivat - , quantitative predictions for training time, transfer, and
execution time for various tasks.
Kieras ark] Blair (1986), Poison and Kieras (1985) and Polson et al.
(1986) awry others have su~-cctully tracts assumptions underlying
these predictions. These authors have Shown that the amount of time
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1
184
r~ui~ to learn a task is a linear function of the namer of near
rules that ~st be acquired In order to successfully execute the task
arxt that execution time is the sum of the execution times for the rules
that fire in order to complete the back. They have shown that transfer
of training can be Characterized in the terms of Charm nlles.
-
I~ANS~ ~X OF USER S=T ,T .R
In a follow ~ section, rcs ~ on transfer of user skills ~
human-computer interaction will be reviewed. m is research shows that
it is possible to give a very precise theoretical characterization to
large transfer effects, reductions in training time on the order of
three or four to one. m ese results strongly support the hypothesis
that large transfer effects are due to explicit relationships between
different tasks performed on the same system or related tasks performed
on different systems. Existing models of the acquisition and transfer
of cognitive skills enable us to provide precise theoretical
descriptions of these transfer processes. mese same models can in
turn be used to design consistent user interfaces for a wide range of
basks and systems that will promote similar large reductions .un
training time and saving in training costs.
A m eoretica1 Model of Positive Transfer
The dominant theoretical approach for explaining specific transfer
effects is due to m Orndike and Wood ward (1901) and m orndike (1914~.
m Orndike assumed that transfer between two tanks is mediated by common
elements. Common elements acquired in a first task that successfully
-
generalize to a second do not have to be re~earned *tiring the
acquisition of the C--r-~nd bask. If a large number amount of the
knowledge required to successfully perform the second task transferred,
there can be a dramatic reduction in training time.
Kieras and Boxcar (1986) and Poison and Kieras (1985) pro posed that
a common elements theory of transfer could account for positive
transfer effects *tiring the acquisition of operating procedures. The
common elements are the rules. Tasks can share methods and sequencer
of user actions and cognitive chelations. These shared ccmponents are
represented by common rules. It is assumed that these shared rules are
always incorporated into the representation of a new task at little or
no cast in training time. -thus, for a new task in the m~1e of a
training sequence, the number of new unique rules may be a small
fraction of the total set of rules necessary to execute this task.
Examples of Successful Transfer
Thin section briefly describes results from the human-computPr
interaction literature demonstrating the magnitudes of the transfer
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185
effects and showing how COT (Kieras and Poison, 1985) can explain these
rats.
Poison et al. (1986) found very large transfer effects, on the order
of four to one reductions in training time, for learning to perform a
simple utility bask on a menu-based, stand-alone, word processor.
Their theoretical analysis showed that a significant portion of the
knowledge, when quantified in terms of number of rules, required to
perform these tasks were in consistent with low-level methods for
making menu transitions, entering parameters, and the like.
Singley and Anderson (1985) found large transfer effects between
different text editors, e.g., transfer from a line to a screen editor.
Poison, Bova~r, and Kieras (1987) found effects of similar magnitude
for transfer between two different scream editors. Their theoretical
analysis showed that editor= share common top level methods that
deccmpcee the task of editing a manuscript into a series of subt~sks
involving indivi~1 changes in the manuscript. Furthermore, ever very
different editors share low-level methods, e.g., cursor positioning.
Text mating is a task where transfer is mediated by knowledge of the
general structure of the task as well as shared methods.
m e Xerox SIAR is a workstation that was explicitly designed to
maximize the transfer of methods both within a given application as
well as across different applications (Smith et al. 1983). All
commands have a common format. m e user first selects an object to be
manipulated using specialized selection methods for different kinds of
text or graphic objects. m e operation is selected by pressing one of
four command keys on the keyboard. For example, hitting the delete key
causes the selected object to be deleted.
Ziegler et al. (1986) carried out transfer experimeents with the SIAR
~ . · . . . . . . .
_
· . · · . . .
workstation. m ey studied transfer between text and graphics editors.
m ey showed that common methods acquired in one context were
successfully transferred to the other leading to very large transfer
effects. Further, they were able to provide a quantitative analysis of
the magnitude of these transfer effects using a production system model
like those of Polson et al. (1987).
An Example of the Impact of Low Level Inconsistencies
Karat et al. (1986) examined transfer between three highly similar word
processing systems that were intended by the Or designers to facilitate
the transfer of user skills frown one system to another. The first
system was developed as a menu-basc5, stand alone word processor. A
major goal in the design of the follow-on systems was to facilitate
transfer from the dedicated/ stand-alone/ word processor to word
processors hooted on a general purpose persona Q computer and a
departmental computing system.
Karat et al. evaluated the magnitude of transfer effects from the
dedicated version of the system to the other two system environments.
The transfer effects were disappointingly small. Karat et al. found
users' difficulties transfers mg their skill were due august entirely
to subtle differences in low level-methods. For example, many problems
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186
were caused by the fact that the dedicated version of the system has
specialized, labeled faction keys. C>n the genera purpose personal
Sprouter and Ache depar~taQ c~ui:er system versions, the user had to
yearn and re = In the locations of Ache car ~ porting functions on an
unlabeled, generic keyboard. Inconsistencies in key assignments for
activating known functions disrupted performance when users attempted
to transfer their skills from one version of the system to another.
Implications for the Design of Systems in the Space Station
m e research reviewed In preceding sections shows that common method=
are transferred across harks and application leading to large
reductions in training time, on the order of 100% to 300%. However,
the Karat et. al. results show that these transfer effects are fragile
and can be reduced by minor but arbitrary differences In low-level
methods let alone more extensive inconsistencies. For example, the
method for centering text is identical on both the dedicated and
personal computer versions of the systems except that the centering
function is activated on the dedicated version by control-Shift C and
by Control-Shift X on the personal computer version. This small
inconsistency disrupted the performance of skilled users of the
dedicated version forcing them to stop and reefer to documentation to
find the correct function key. This Inconsistency was caused by the
fact that Control-Shift C already used by many applications programs to
abort and return to the top level of operating system.
The potential for serious inconsistencies In common Seth ~= across
different systems and application in the Space Station is much greater
than the example of the tier=" word processing system stied by Karat
et. al. They were all developed by a single manufacturer with the
explicit goal of permitting transfer of skills developed on Me
dedicated version of the system.
_,~
TENSION OF REX VISIIAL DISPIAYS
Rapid developments in hardware arm software technology permit vie
generation and presentation of very complex displays cc~3inir~ text,
color, motion, arm complex visual representations. There is liming
urxierstar~ing of hear to effectively utilize these new capabilities.
There is excessive h~-riedge of the basic visual p ~ cesses urxierlying
color and form perception (Graham, 1965; Wairaven, 1985~. Detailed
models of the comprehension of complex visual displays do not exist.
There is some systematic work on the effective graphical presentation
of quantitative information (e.g., Kosslyn, 1985; Tufter 1983~. The
widely acclaimed book The Visual Display of Ouantitative Information by
Tufte is a collection of design guidelines.
Today, development of effective complex displays reties alma st entirely
on err~rically-based, iterative design methods (Gould and Lewis,
1985~. A good illustration of how effective these methods can be is
shown in an experiment reporter by Burns et al. (1986~. These
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190
Operative ~man~uter Emblem Solvers
NPSA's gals are far more Editions Man the devel~rent of autonomous
intPllig~t prim solvers with explanation capabilities. It is
repeat propose in various NMA docents to develop cooperative or
symbiotic h~nan~r~uter pr~iblern solvers (Jason et al. 1985; Arxierson
arm Is, 1985).
Discussions about the possibility of de~relopir~g such systems have a
surprising uniformity. me authors Ire that powerful problem
solvers can be developed if systems exploit the complimentary strengths
of human and machine permittir~g one to Sate for the weaknesses of
the other. The ne ~ issue is function allocation. The discussion of
function allocation begins with a general assessment of the strengths
and weaknesses of human and computers as problem solvers. m is
assessment is ~ the form of a characterizations human and machine
components listing the strengths and weaknesses of each. Typical
listings are in Johnson et al., 1985, pp. 27-28; Richardson et al.,
1985, pp. 47-49; Anderson and Chambers, 1985. What is striking about
these lists is the ~ consistency. the following is taken from
Richardson et al. (1985, pp. 47-49).
the strengths of the human component of the system are:
1. Processing of senso ~ data.
2. Pattern recognition.
3. Skilled physical manipulation but limited physical strength.
4. Tempted metacagnitive skills, e.g. ability to reason about
1imlts of knowledge and skill.
5. Slow but powerful general learning ~ isms.
6. A large, content-addres-c~hle permanent memory.
The weaknesses of the human problem solver are:
1. Limited working memory.
2. Limited capacity to integrate a large number of separate facts.
3. Tendency to perseverate on favorite strategies and malfunctions;
set effects an] functional fixity.
4 . T.i~; ted induction capabilities.
5. Lack of consistency; limitations on the ability to effectively
Mae new information.
Fn~tior~al arm motivational problems.
T;nlitations on the availability of individuals with the
necessary abilities and skills.
Limited endurance.
6.
7.
8.
The current generation of expert systems and highly autonomous
automatic systems, e.g. AIE's make use of human sensory processing,
pattern-r~nition, and manipulative skills. Most authors recognize
this and point out that their objective In developing cooperative
problem solacing systems is to exploit human's cognitive capabilities as
well as these lower level skills. Continuing to quote Richardson,et
al., the strength of the computer component of the system are:
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191
1. Large processing capacity.
2. Large working memory.
3. Capabilities of making consistent mechanical inferences taking
into account al' relevant facts.
4. Processing and utilizing large amounts of actuarial information.
5. Capabilities to store and retrieve training and reference
material.
6. Availability of system is limited only by reliability of basic
computer technology.
7. No motivational or other related problems.
The weaknesses of the machine component of the system are
I. No or very limited capacity to adapt to novel situations.
2. No or very limited rearm ng abilities.
3. No or very limited m eta ~ tive abilities, e.g., understanding
of own limitations.
4. Very difficult to program particularly the current generation of
expert systems.
Examples of Cooperative Systems
The test examples of cooperative systems are intelligent training
systems (ITS) (Sleeman and Brawn, 1983; Poison and Richardson, 1987~.
The main components of an T1~ are: 1) the expert module or task
model, 2) the student module or user model, and 3) the tutor module
or explanation subsystem. A cooperative, intelligent problem solving
aid has to have real expertise about the task, an accurate model of the
other intelligent agent that it is interacting with (the human user),
and the capability of coixtucting sophisticated dialogues with the
user. Richardson et al. (1985) argue that the machine component
should attempt to compensate for known limitations and failure modes
that are characteristics of all forms of human problem solving: They
are working memory failures, set and functional fixity, inference
fat Ares, and attentional limitations.
One important role for a cooperative intelligent system would be to
reduce information overload by selectively displaying information
relevant to the highest priority subcomponent of a bask. Chambers and
Nagel (1985) describe the cockpit of a Boeing 747 with its several
hundred instruments, indicators, and warning lights as an example of
where skilled pilots can be simply overwhelmed by the amount of
available information. Plans for highly automated aircraft of the
399Os incorporate selective displays on color CRTs of a small subset of
the tote] information about the state of the aircraft that is relevant
to the current task. The ability to display relevant information would
prevent information overload and augment human working memory by
providing an external representation relevant information about the
system's state.
Other propceals for the role of the computer In a cooperative system
focus on its computational capabilities. Memory limitations prevent
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human users from adequately integrating information about the current
state of the system and archival information concerning likelihoods of
component fat ures. Thus, the machine takes on the role of files',
and inference engine compensating for known general
memory aid, _ ~ _
weaknesses in the human information processing system.
Possible Scenarios - Serious Problems
Ih~e proposals are consistent with the large body of data about the
starch and weaknesses of human diagnostic reasoning arm prablem
solving. Hawed, implementing these proposals And a functioning
system can cause serious difficulties. Consider a situation involving
the power distribution system of the Space Station where sever
Attracting failures have Curry. The system makes a series of
Encore ~ inferences about ache cause of he faults and displays to the
human partner information irrelevant to successful solution of the
problem. Such misinformation could effectively block successful
solution by the human user. It's essentially a set manipulation. The
misinformation would be especially damaging if the system were normally
su~==ful.
Other problems could result if the system makes incorrect inferences
four its model of the human user. Assume the system has conclude d,
cornily, that is is incapable of independently diagnosing the faults
in the pCW=r distribution system. Using its advanced explanation
capabilities, it explains to its human partner its understanding of the
current state of the power distribution system and various martial
A_. ~ _ _~e _!__= '_ _ ~ ~ - I!__ =_ =! ~ I_ ' I._ __
AQUA w~a=~ =~ Al ~~ Al A. In ache Process.
, _ _ _ _ _ _ , _ _ _,
~ . · _ · ~ . .. . . .
system presents a series or complex ads prays snowing the current state
of the power distribution system. The expert human user recognizes a
complex pattern of interrelated events and informs the cc mputer of the
correct solution to the problem. The system responds by attempting to
evaluate the human partner's in put using information contained in its
user model. This model has a very detailed description of the limits
of the human information processing system, and the system incorrectly
concludes that the human partner is Capable of making the correct
diagnosis on the basis of such complex input and the solution is
rejected.
Conclusions
__)
Many readers may think that the scenario presented in the preceding
section is overdrawn. Of ccNrse, NASA would never tolerate the
fielding of a system that was capable of effectively overruling a Space
Station crew member. However, a system ~ which human users can
Override the machine partner compromises the goal of developing truly
cooperative human-oomputer problem solving systems. Information
Overload, working memory failures, and failures to integrate historical
data in making diagnoses are highly probable failure modes of human
users. me incorrect inference made by the machine described in the
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193
preceding scenario is not unreasonable and would probably be correct in
most situations. Experience with intelligence tutoring systems (Poison
and Richardson, in press) shows that such cooperative systems are
exceedingly difficult to construct.
REGIONS FOR MAR RESEARCH AND CONCLUSIONS
This section contains information on recommendations for further
research and concludes that the difficulties in developing truly
productive ccmputer-based systems are primarily management problems.
Information Processing Models
Peccmmendation 1. Support the development of the software tools
required to rapidly develop information processing models of tasks
performed on the Space Station.
m is chapter has recc=mcnded that information processing models of
cognitive processes be the basis for the design of applications
programs, complex visual displays and cooperative human-computer
problem solving systems. A theoretical technology should be applied on
a large scale to solve interface design problems on the Space Station.
Unfortunately, the development of information processing models is
currently an art and not a robust design technology. Furthermore, these
models can be extremely complex simulating basic psychological process
in detail (Anderson, 1983). What is required are engineering models
(Newell and Card, 1986; Kieras & Polson, 1985).
Development of an effective modeling facility is an engineering
problem, albeit a difficult one. There are no advance= required in the
theoretical state of the art in cognitive psychology. Models of
various cognitive processes have to bee integrated into a-single
simulation facility, e.g., models of perceptual, cognitive, and motor
processes. Higher level languages should be developed that automate
the generation of the simulation code and the detail derivation of
models. A simulation development system will be required for designers
to rapidly develop models of adequate precision for use in a timely
fashion in the design process.
m e Comprehension of Complex Displays
Recommendation 2. Support an aggressive research program on the
processes involved ~ the comprehend sion of complex, symbolic displays.
Many tasks on the Space Station will require that crew members
interact with complicated displays. Examples include monitoring and
trcNble shooting of complex subsystems, manipulation and presentation
of scientific data, and interacting with expert systems to carry out
trouble shooting and maintenance tasks. Rapid advances in computer and
OCR for page 194
194
display technology will enable designers to develop complex displays
making using of symbolic, color, and motion cues. Effective displays
that facilitate performance on these complex tasks can have large
positive effects on crew productivity. The complexity of the tasks and
the freedom given to the designer by the display technology require
that successful designs be based on explicit models of how information
in such displays is used to perform these Caulks.
Develcpment of models of the comprehension of complex displays
requires important contributions to cognitive theory. Current research
~ cognition and perception provides a solid foundation on which to
build such models. It is possible that models of comprehend ion of
complex displays can be based on the extensive body of theoretical
results obtained on the processes involved in text comprehension (e.g.,
van Dijk and Kintsch, 1983~. Excellent work on related problems is
already going on within NASAL research programs in this area could be
modeled in the work of Ellis and his colleagues briefly described in a
preceding section.
Human-Computer Problem Solving
Recommendation 3. Design and support an aggressive research program
leading to the eventual development of cooperative, human-computer
problem solving systems.
Although the many analyses characterizing cooperative human-computer
problem solving are correct, development of a useful cooperative system
r y es solutions to unsolved problems in expert system design,
artificial Negligence, and cognitive science. A well structured
research program would generate many intermediate results, components
of the eventual cooperative system, that are useful in themselves on
the Space Station. These include robust, high performance expert
systems, advanced expla ~ tion subsystems, and various problem solving
tools to assist the crew in management of the Space Station systems.
Consider utilities of an inspectable expert system and of an
inference engine tool. By an inspectable expert system, we mean a
system that displays intermediate states of its diagnostic processes
during trouble shooting. The expert systems tool presents to the
trained user intermediate results of the trouble shooting process using
of complex, symbolic displays. Properly designed, such information
gives the human expert the information necessary to confirm a diagnosis
or take over effectively if the expert system fails. Most current
automatic fact equipment simply reports success or failure, e.g., a red
light or a green light. An inspectable expert system would be a
dramatic improvement over diagnostic systems with such limited
feedback.
Another useful subsystem would be a inference engine, a tool that
combines information about system state with actuarial data on the
likelihoods of different failure modes. This system would be designed
to enable a skilled human user to do what if calculations and serve as
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195
a memory aid reminding the crew member of infrequently occurring faults
that are likely to be overlooked.
Inspectable expert systems are within the state-of-the-art an] would
serve as a very Graceful test bed for research on comprehension of
complex symbolic displays and on the design of such displays. An
interactive inference engine could be seen as a primitive prototype of
a cooperative problem solving system. Both tools can be very useful In
an operational environment and both are important intermediate steps in
the eventual development of high performance cooperative systems.
There are important areas of research In cognitive science that will
have to be better developed before it will be possible to build
successful cooperative human-computer problem solving systems. These
include models of human diagnostic reasoning, cooperative problem
solving, and models of the processes involved in generating and
comprehending useful explanations. A cooperative system must
incorporate an extremely sophisticated model of its human partner which
in turn requires a detailed understanding of how humans carry out the
specific task performed by the system as well as the general
characteristics of the human information processing system and its
failure modes. User models are related to the problem of developing
student models in intelligent training systems. Although program= is
being made in the area of student modeling, there is still numerous
important unsolved problems (Poison and Richarson, 1987~.
In summary, the design and development of cooperative,
human-co mputPr problem solving is the most difficult of the
technologist goals related to cognitive science associate] with the
Space Station. This goal will only be achieved by a long term, well
managed research program.
In Reality, It's a Management Problem
It is widely recognized that the ambitious productivity goons for the
Space Station can only be achieved with extensive use of autcmateJ
systems that have effective user interfaces. However, there is a broad
gap between good intentions and actual development practice. It is
widely recognized today that complex systems developed for civilian,
NASA, and military use are far frog the current state-of-the-art in
human factors presenting serious problems for their users. Often,
design errs are so obvious that applications of simple common sense
could lead to the development of more usable interfaces.
In the final analysis, development of usable systems is a management
problem. Consistent application of the current state-of-the-art in
human factors and knowledge of cognitive processes during all phases of
the develcpment process would have dramatic and positive effects cn the
productivity of the Space Station crew.
OCR for page 196
196
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Representative terms from entire chapter:
training time
