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AI SYSTEMS IN THE SPACE STATION
Thomas M. Mitchell
INIRODUCI1ON
Among the technologies that will help shape life in the space station,
Artificial Intelligence (AI) seems certa m to play a major role. The
striking complexity of the station, its life support systems, and the
manufacturing and scientific apparatus that it will house require that
a good share of its supervision, maintenance, and control be done by
computer. At the same time, the need for intelligent communication and
shared responsibility between such computer programs and space station
residents poses a serious challenge to present interfaces between man
and machine. Hence, the Potential and nope for contributions from AI
to the space station effort is great.
The purpose of this paper is to suggest areas In which support for
new AI research might be expected to Produce a significant impact on
future space station technology. Given the breadth of this task, the
approach here will be to sample a few such ureas and to rely on the
other symposium participants and other sources (e.g., Technical Report
N~-ASEE, 1983; Tedhnical Report Ned, 1985) to fill in the picture.
More specifically, we will address here (1) the use of knowledge-based
systems for monitoring and controlling the space station, and (2)
Issues related to sharing and transferring responsibility between
computers and space station residents.
Before focussing on the specifics of these two problem areas, it is
useful to understand their significance to the development of the space
station (and to other advance projects such as development of a lunar
base and interplanetary probes).
In his keynote address to this symposium, Allen Newell provides an
analysis of the general characteristics and constraints that define the
space station effort.
include the following:
Those of particular relevance to this paper
The station is an extraordinarily complex system` with an
extremely high premium to be placed on reliability, redundancy,
and failsafe operation. In past space efforts, a large share of
astronaut train mg has gone into acquiring the knowledge needed
to supervise, control, and troubleshoot various spacecraft
91
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92
subsystems. The Increased c~nplexit~r of the space station
argues for c~uter-bas~ assistance in the supervision of many
station subsystems, and it is no surprise that ache history of
the space program is a history of increasing automation ark
chanter su~rision.
F=th~re, the high premimn on failsafe
operation place= spry canards on the flexibili~r arxt
adaptability of such ~uter-based supervisors.
. . .
Subh system
must be flexible enough to recognize and a~pt to unanticipated
events, ark to fornicate such unanticipated events clearly to
the hens who help choose ache rinse to these events. The
flexibility d ~ reed here goes well beyond that associated with
present-day computer based supervisory systems.
The space station is intended to be a highly evolutionary
system, which will be continually reconfigured and upgraded over
the course of its lifetime in space. The highly evolutionary
nature of the station will make the task of crew train mg even
move difficult than if the station were a static system. The
problem of updating operating and troubles hoofing procedures
will be greatly exacerbated. In general, there will be greater
demands on maintaining and updating the external documentation
. · . . . . . . . . .
or the space station subsystems, and on prompt, thorough
updating of procedures for monitoring, controlling, and
troubleshooting the evolving space station. Ccmputer-based
methods for automatically updating such procedures, given
updates to the description of the space station, would greatly
enhance the ability to manage the evolving station.
The crew of the space station will possess differing levels of
expertise regarding different space station subsystems, and will
live in the station long enough that then' expertise will change
over the course of their stay aboard the Station. These
differences in level of sophistication among various crew
my; (and between the same crew mar at differing tone
pose significant pray; ark Opportunities for the ~ter
system with which they will interact. For naive users,
~ter synods that ~ given actions will have to
provide a fairly detailed explanation of ye reasoning behind
the ~ ation. For more ex ~ rt users, 1-cc explanation may
be needed. For advanced users, there will be an opportunity for
the computer system to acquire n ~ problem-solving tactic_ from
the users. Furthermore, as a particular user b , familiar
with the competent= and limitations of a particular
cc: puter-h=~P~ supervisor, his willingn=-c~ to allow the system
to make various decisions without human approval may well
change. The ability to interface effectively with a range of
users, acting as a kind of tutor for some and acquiring new
excise fan others, ~d allow the coercer to act as the
"corporate marry" for the particular abet of the Apace
station bat is its dc~ n and for Rich it will house a
continually e~rolvir~g set of excise.
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93
BORING, DI~NOS~G, AND C~N~T,T.T~G THE SPACE STATION
Given the abase characteristics of the space station effort, it is
cheer that the use of ~ter-h~ assistants for supervising various
space station subsystems card have a major impact on Be overall
reliability and cost of space station Operations. In order to develop
such cc~put~r-bas~ supervisors' - tic YCh is needed in a ~ er
of areas such as representing and reason ng about complex designed
artifacts, inferring the behavior of such systems from schematics
showing the Or structure, and automatic refinement of supervisory
procedures based on empirical observation as well as the known system
schematics.
since the space station will itself be a large, well-documented
artifact, it ~ reasonable to expect a significant number of
a . ~ _ _ ~ ~ ~ ~ ~ ~ ~ _ __e __ ·~
~ _
opportunities For applying computers to One cask or supervising,
controlling and diagnosing the space station. For example' one might
well expect that a computer could monitor various space station
subsystems such as the parts of the navigation system, to detect
behavior cutside their expectcS operating ranges, take remedial actions
to contain the effects of observed errors, diagnose the likely causes
of the observed symptoms, and reconfigure the system to eliminate the
error. Of course, lim;t=~ applications of computers to this king of
problem are fairly common in current-day space systems. But present
met hods for automated monitoring, diagnosis and control are far from
the levels of generality, robustness, maintainability, and competence
that one would desire. AT offers a new approach to the problem of
automated ~vision. With appropriate research support, NASA might
expect to significantly accelerate the devel~nt of AI methods for
deal ing with this class of problems, and thereby provide important near
tedhnolow to support the space station.
,,
_ _ ,, ~ , ,
A Roger of recent AI system; have addressed problems of monitoring,
diagnosing, or controlling designed artifacts such as o~uter systemic
(Ennis et al., 1986), ele*m~ani~1 systems (Pazzani, 1986),
dhemi~1 pareses (Scull et al.. 19851. ark Mini—1 circuits (Davis,
1984; Ger~esereth, 1981~.
Prom this work, an initial set of techniques
has emerged for ~1~ after programs that eddy a Mel (often
in qualitative terms) of the behavior of the system Waxier study, and
which use this Mel to reason about the diagnosis, control, or
reconfiguration of the system. Mile much remains to be ur~ersto~,
the initial approaches have Chain clearly the potential for super~risory
Outer systems that combine judgemental heuristics with reasoning
fray a concrete Mel of the systems under stud.
An Example
As an example of an AT system that deals winch monitoring and
troubleshooting a designed artifact, consider Davis' circuit
troubleshooting system (Davis, 1984~. This system troubleshoots
digital circuits, given a schematic of the misbehaving circuit together
with den discrepancies between predict and observed signal
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94
values. Its organization is Typical of several troubleshooting systems
that have been developed for electronic, mechanical, and other types of
systems.
The basic idea behind this troubleshooting system is that it uses
the schematic of the system, together with its knowledge of the
expected behaviors of system Opponents, ~ order to reason backward
from observed incorrect output signals to those upstream circuit
Opponents that could have product the cbservec} error. This process
is i'1ustrat~ in Figure 1, tin from Davis (1984~.
In this figure, if the circuit inputs are given as shown, the system
will infer the expected outputs as shown in round parentheses, based on
its knowledge of the behaviors of multipliers and adders. If the two
observed outputs are ~~ shown in square parentheses, then a discrepancy
is found between the expected and observed values for signal F. The
system will then enumerate candidate fault hypotheses by considering
that the error may be due to a failure in Add-l, or to incorrect values
for one of its inputs (either X or Y). Each of these last two
hypotheses might be explained further in tRrms.of possible failures of
the components or signals on which it, in turn, depends. Thus,
candidate fault hypotheses are enumerated by examining the structure of
the circuit as well as the known behaviors of its components.
In addition to enumerating fault hypotheses ~ this fashion, the
system can also prune these hypotheses by determining other anticipated
consequences of presumed faults. For example, the hypothesis that the
error in signal F is caused by an error in signal Y. carries with it
certain implications about the value of signal G. The value of 10 for
signal F can be expla med by a value of 4 for signal Y. but this would
in turn lead to an expected value of 10 for signal G (which is observed
to hold the value 12~. Hence, this hypothesis may be pruned, as long
as one assumes that the circuit contains only a single fault.
The above example illustrates how a computer system can reason about
possible causes of observed faults, by using knowledge of the schematic
of the faulty system as well as a library describing the expected
behaviors of its components. There are many subtleties that have been
A
2 —
2 —
3 —
_ _
3 - Mult-1 , x
Add-1 — (12)
~ r [10]
Y
l _ G
Add-2 (12)
r [12]
MUII-~ z
Expected-( )
Actual---[ ]
FIGURE 1 Troubleshooting example. Source:
Davis (3984~.
OCR for page 95
95
glossed over ~ this example, such as reasoning about the possibility
of multiple system faults, interactions between faults, intermittent
errors, utilizing statistical knowledge of likely faults and the
resulting faulty behavior, scaling this approach to more complex
systems, and the like. Relic research is still needed to develop more
realistic diagnostic systems of this sort, and many of these issues are
under study at this time. In addition, a good deal of research has
been devoted to developing similar troubleshooting systems for
artifacts other than digital circuits (e.g., mechanical
electrcmechani~=l, and chemical processes). The topic of reasoning
about the expected behavior of designed artifacts of many types is an
active research area within AI (see, for example, the recent special
volume of Artificial Iht=1ligence on qualitative reasoning about
physical systems (North-Holland, 1984~.)
hands-On Supervisory Systems
The above example is meant to suggest how a program can utilize an
internal model of the system it is monitoring in order to localize the
cause of anomalous behavior. Since the space station will be heavily
instrumented with sensors and with comput~r-controlled effecters, the
real opportunity here lies in developing a technology for "hands-on" AI
supervisory systems: systems that have the means to directly observe
and control the behavior of systems that they monitor, and that possess
an explicit model of the system under supervision to guide their
reasom ng about monitoring, controlling, and troubleshooting this
system. Figure 2 illustrated the general organization of such a
hands-on supervisory system.
One instantiation of the scenario characterized In the figure could
be an electronically self-sensing, self-monitoring space station. Here
the system under supervision is the space station, sensors may observe
the temperatures, pressures, and electrical behavior of various
subsystems of the space station, and effectors may correspond to
electrically controlled devices such as signal generators, heaters,
compressors, and alarm systems. m e goal of such an intelligent,
serf-m anchoring space station would be to observe its behavior through
its sensors, comparing these observations to the behavior anticipated
by its internal model, an] utilizing its effecLors to maintain stable
operation, reconfigure subsystems, and control the trajectory of star-=
of the system. A number of observations are apparent about such a
system: To a limited degree it is already possible to build such
partially ~elf-mon~toring systems. m e theoretical possibilities for
computer monitoring and control in such systems far exceed the
capabilities of cur present techniques. m e effectiveness of such a
system will depend on continuing fundamental research In AI, especially
in areas such as qualitative reasoning, diagnosis, control, and
learning. Tb allow for such a future, the initial design of the space
station must al low for flexible introduction of new sensors and
effecters In all subsystems of the space station, and over the entire
life of the station.
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96
Supervisory System
1 of System I r
Under Supervision
Ef factors
~ I
Prob Tom So leer
Sensors
I
I
I
l
FIGtIllE: 2 Hays on s~isory system.
A very different instantiation of ye scenario of Figure 2 is
ebb nect by inducing mobility ~ e sensors and effacers of the
~ter monitor. ~ this case, the s~risor card take ye form of
a collection of mobile platforms whose sensors include eras, range
finders, Much sensors, and oscilloscope pubes, and whose effe~ors
include gels, r~et engines, manipulators, signal generators, arm
arc welders. Such a system might be ~1 to zanier the Physical
plant of the space station, c) Sing for wear, arm repairing the
station as necessary, boo cerior and exterior. Several privations
follow from considering this scenario: The leverage gained by avid
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97
mobility to sensors and effecters is large--especially in situations
such as troubleshooting where the system parameters in question might
not be directly observable or controllable by statically positioned
sensors and effectors. A number of difficult issues arise in
representing and reasoning about three dimensional space, navigation,
and the mechanics of physical systems. Given previous experience with
robotics, it is clear that the difficulty of the technical problems can
be considerably eased by designing a well-engin==red work environment
(e.g., by including easy grasping points on objects that are to be
manipulated in the space station.
In fact, we would like cur supervisor to possess a ccmbination of
mobile and stationary sensors and effectors, including the union of
those in the above scenarios. Thus, these two scenarios illustrate
different aspects of the class of hanger-on supervisor problems
summarized in Figure 2. The two scenarios suggest a number of common
technical problems, including problems of integrating human judgement
with computer judgement, planning a sequence of control operations
based on only an incomplete model of the system under supervision, and
utilizing sensory input to ref me the model of the system undo'
supervision. At the same time, each scenario carries it own technical
problems which overlay those generic issues. For example, a mobile
supervisor for monitoring and repairing the exterior surface of the
space station most fare issues such as representing and reasoning about
tier== dimensional space and navigation, interpreting a rich set of
perceptual data taken from a changing (and incompletely known) vantage
point, and using tools to manipulate the space station. Thus, NASA
Should consider supporting research on the generic problems of Harrison
su~isory system;, as well as research on selected instances of the
problem which it expects would yield significant practical gains.
Nature of the Problem
A fundamental defining characteristic of the system supervisor problem
is uncertainty in the supervisor's knowledge of the system under
study. A supervisor can almost never have complete and certain
knowledge of the exact state of the system, of the rules that determine
how one system state will give rise to the next, or of the exact
effects of its control actions on the system. This characteristic
alters dramatically the nature of diagnostic and control tasks. For
example, given a perfect model of the system under study, a program
might derive an open-loop control sequence to place the system in some
desired state. However, in the absence of a perfect model, controlling
the system requires interleaving effecter actions with sensory
observations to detect features of the system state.
m e types and degrees of uncertainties faced in system supervision
problems vary, of course, with the specific task. For instance, the
task of monitoring a digital circuit might correspond to an extreme
point in the spectrum of possibilities, since circuits schematics do,
in fact, provide a very detailed model of the system, and since
observing digital signal values is (by design) a relatively unambiguous
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98
Bask. It is probably no accident ~t several of the -earliest attempts
to construct AI troubleshooting aids were conducted ~ the domain of
digital circuitry. Ha~rever, that work shared Cat even ~ this dc~
it was very difficult to troubleshoot circuits teas ~ only on the
knowledge available fen the circuit schematic (Davis, 1984~. the
pr~blen is that circuit behavior can depend on thermal effects,
physical proximity of ~nents, and other factors which are not
typically reflected in a circuit schematic. Furthermore, it is
precisely in troubleshooting situations that such effects become
significant to determining the system's behavior. The problem of
incomplete knowledge in modeling subsystem behaviors is even more
difficult when one considers systems with combinations of electric=.],
mechanical, chemical, and biological subsystems.
In addition to uncertainly in modeling the expected behavior of the
system under study, the difficulty of interpreting sensory input adds
another kind of uncertainty in many domains. In the digit al circuit
world, it is fairly straightforward to observe the value of a desired
signal, though it is rare that circuits are constructed so that every
signal is brought outside the circuit for troubleshooting purposes. If
the system under study is a chemical process rather than electrical,
detecting relative concentrations of chemicals can often be a more
complex task. In mechanical systems, detecting enact locations and
forces is generally cut of the question. If the system is the exterior
of the space station and the sensors are video cameras, then the
Iffily of sensing the exact location and physical condition of each
subcc=ponent can itself become such an overwhelming Mask that the
Cations themselves must be treated as uncertain.
Yet Another dimension of uncertainty arises from the effecters that
are utilized by the supervisor to alter the system under study. Again,
in the circuit domain effecters such as signal generators are
relatively reliable. But In the robotics domain, in which the system
being supervised is the physical world, effecters such as artificial
limbs may be fa Ply unreliable in executing actions such as grasping.
In such ~==C, the problem of planning a sequence of actions to bring
the system to a desired state must take into account nondeterminism in
the effect of actions it performs.
In a sense, the ability to observe and affect the system under study
and the able ity to predict its behavior provide redundant scurces of
knowledge so that one can be used to make up for uncertainly ~ the
other. For instance, feedback control methods utilize sensory
information to make up for an incomplete model of the next-state
function. On the other hand, one can make due with observing only a
small proportion of the signal values in a circuit and ,,.c~ the model of
suboc~ponent behaviors to infer additional signal values upstream and
downstream of observed signals.
Given the various uncerta Sties that must be faced by a supervisory
system, it is unlikely that purely algorithmic methods can be mapped
out for dealing with all eventualities (although the vast NASA
troubleshooting manuals indicate the degree to which this ought be
possible). A supervisory system will do best if it possesses
redundancy to make up for the uncertainties that it must face:
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redundancy Ln the sensors that give it information about the world, in
the effecters with which it controls the world, and in the behavioral
models that it uses for reasoning about the system under study. While
such redundancy can help reduce uncerta Sty, it will not be eliminated,
and the supervisor must therefore employ problem solving methods
designed to operate under incomplete information. All of these napes
suggest the importance of combining heuristic methods with deductive
methods for reasoning about the system under study. Finally, these same
problem characteristics that suggest the utility of employing AI
methods (the need for flexibility in solving problems despite
uncertainty) also suggest the Importance of including humans in the
problem-solving process. Even by optimistic estimates, it seems
unlikely that AI systems will be able to completely replace human
judgement in many supervisory tacks, though they may well augment it in
many tasks. m us, in many cases we envision cooperative problem
solving involving computer systems and humans. Section "Sharing and
Transferring Expertise in M~n-~achine Problem Solving" discusses issues
related to man-machine cooperation in this regard.
Research Recc==endations
What research should be supported by NASA in order to maximize the
future availability of hanger-on supervisory systems of the kin]
described above? m is section lists some areas that seem especially
important, though the list is certainly not intended to be complete.2
· Modeling system behavior at multiple levels of abstraction. At
the heart of the ability to supervise a system lies the ability
to model its behavior. Systems theory provides one body of
(primarily guantitative) techniques for describing and reasoning
about systems. AI has developed more symbolic methods for
describing and reasoning about systems, given a description of
their parts structure. A good deal of research is needed to
further develop appropriate behavior representations for a
variety of systems at a variety of levels of abstraction, and
for inferring behavioral descriptions from structural
descriptions. In addition, work is needed on automatically
selecting from among a set of alternative models the one most
appropriate for the task at hand. For example, one useful
research task might be to develop a program which can be given a
detailed schematic of a large system (e.g., a computer) as well
as a particular diagnostic problem (e.g., the printer is
producing no output), and which returns an abstract description
of the system which is appropriate for troubleshooting this
problem (e.g., an abstracted block diagram of the computer
focussing on details relevant to this diagnostic task).
Planning with incomplete knowledge. The planning problem is the
problem of determining a sequence of effecter actions which will
take the external system to a desired state. This problem has
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100
been studied ~nbe.nsely within AI, especially as it relates to
planning robot actions in the physical world. However, current
planning methods make unrealistic assumptions about the
completeness of the rcbot's knowledge of its world, and of its
knowledge of the effects of its own actions. New research is
needed to develop planning methods that are robust with respect
to uncertainties of the kinds discussed above. One usefrn
research task here would be to develop methods that pro~u~-
plans which include sensor operations to reduce anticipated
uncertainties in the results of effecter actions, and that
include conditional branches in the plan to allow for "run-time"
derisions based on sensory actions.
.
.
Integrating methods from control theory with symbolic control
methods. Problems of system control, diagnosis
(identification), and monitoring have been studied for some time
in fields such ~~ system control theory. Such studies typically
assume a quantitative, mathematical model of the system under
supervision, whereas AI methods model the system in a symbolic,
logical formalism. System theory has developed various methods
for using sensory feedback to make up for uncertainty in the
model of the system under supervision, but these methods are
difficult to apply to complex planning problems such as
determining a sequence of robot Aerations to repair a failed
door latch. Still, both fields are addressing the same abstract
problems. Very little attention has been paid to integrating
these two bodies of work, and research on both vertical and
horizontal integration of these techniques should be supported.
Autcmatically refining the supervisor's theory of system
behavior through experience. As discussed in the previous
subsection, a major limitation on the effectiveness of a
supervisor lies in its uncertain knowledge of the system under
supervision. Therefore, methods for automatically refining the
supervisor's knowledge of the system would be extremely useful.
In AI, research on machine learning and automated theory
formation should be supported as it applies to this problem.
m e integration of this work with work in Systems theory on
model identification should also be explored. Possible research
tasks In this area include developing robot systems that build
up maps of their physical environment, and systems that begin
with a general competence in same area (e.g., general-purpose
methods for grasping tools) and which acquire with experience
more special purpose competence with experience (e.g., special
methods for most effectively manipulating individual tools).
Perception from multiple sensors. One method for reducing
uncertainly in the supervisor's knowledge of the system's state
is to allow it to use multiple, redundant sensors. Thus, a
robot might use several video cameras with overlapping fields of
view, placed at different vantage points, together with touch
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sensors, range finder, infrared sensors, etc. Or a supervisor
for mom toring a pow=' Supply system might utilize ~ set of
overlapping voltage and current sensors together with chemical
sensors, hart sensors, etc. The benefits of using multiple
sensors is clears they provide more information. However,
order to make use of the increasing amounts of data available
from multiple sensors, research is needed to develop more
effective sensory 1nterpretation/perception methods for
in~iviHn~d sensors, and for fusing data from several sensors.
An example research task here might be to develop a system that
employs a number of video cameras, and which determines the
correspondence between image features of the various images. A
more ambitious project sight try to predict image features
likely to be found by one camera, based on information from
other touch, video, and heat sensors.
Representing and reasoning about 3D geometric properties. For
supervisors that possess mobile sensors or effectors, a variety
of problems exist in reasoning about navigating through space,
and in reasoning about 3D mechanical linkages such as those that
couple a robot arm to a screw via a screw driver. Research is
needed on representing 3D objects (including empty space) in
ways that allow for efficient computation of relations among
objects. such as intersections. (collisions\. unions possible
J ~ ~ ~ ~ ~ ~
__~1~:_~ ^. _ =~ ~:_~ ~:~1 ~ - ~ - ~~ ..~1A
1~ ~ ~ ~ C ~ · 1' ~ ~ ~11~ C / =~ 1~ ILL ~~= ~~ ~ ~ le Woo
involves constructing temporary mechanical linkages among
objects (e.g., among a robot arm, screw driver/ screw, and
wall), research is needed on efficiently representing and
reasoning about such linkages so that effector commands can be
planned that will achieve desired effects. While
special-purpose robots operating in special-purpose environments
can sometimes avoid using general methods for reasoning about 3D
geometry, general purpose systems expected to solve
unanticipated problems will require this capability.
Designing systems to minimize difficulty in observing and
controlling them. Given the great difficulties in the
supervisory back that we ~ntrodu~ by uncertainty, one obvious
reaction is to try to design the space station to reduce the
uncertainties that auto meted supervisors will face. In short,
the station should be designed to maximize the observability and
controllability of these features which the supervisor will need
to sense and effect. In the =~e of a supervisor with immobile
sensors and effectors, such as a system to monitor the power
supply, this requires that a broad and redundant set of sensors
and control points be built into the power supply at design
time. In the case of mobile supervisors, the observability of
the station can be eng m ~=red, for example, by painting
identifying marks on objects which will ease problems of object
identification and of registering images obtained from multiple
viewpoints. Similarly, the controllability of the physical
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102
space station can be enhanced, for example, by designing all its
parts to present the same simple grasping po Ant. While a good
0~1 of anecdotal experience has been obta med on designing
robot workstations to maximize their controllability and
observability, little exists in the way of a science for
designing such easily-supervised systems. Research in this
area, if successful, could significantly reduce the number of
technical problems that auto meted supervisors in the space
station will face.
Feasibility of replacing hardware subsystems by software
emulations. For immobile supervisors which monitor subsystems
such as power supplies, navigation systems, etc., one intriguing
possibility is that they might be able to substitute additional
computation in place of failed hardware. For example, consider
a subsystem, S. with a failed thermostat, T1. If S is being
supervised by a computer System with a good model of the
suboomponents of S. then this supervisor might be able to keep S
working acceptably by substituting its own simulate cutout of
in, p~ - ~~ ~~. ~¢ ~ ~~ p - : 1 ~ - ~~' - -
L~ '-~ ~1C ~~= Vie ~~ 1~G" ~~- The degree to which
this is possible will depend, of course, on (1) The veracity of
the supervisor is model of S. (2) the access the supervisor has
to other sensors In S (the more redundant, the better), and (3)
the ability of the supervisor to control the point in S
corresponding to the output of T1. While a software simulation
might be slower and less accurate than a working thermostat, the
advantage of substituting software for failed hardware is
clear. Perhaps a small number of high-speed processors (such as
parallel processors that have been developed for circuit
simulations) could be included in the space station precisely
for providing high-speed backup for a wide range of possible
hardware failures. While the feasibility of adding robustness
to the space station by adding such computational power is
unproven, the potential impact warrants research in this
direction.
SHARING AND TRANSFERRING EXPERTISE IN M~N-MACHINE PROBLEM SOLVING
As noted On the previous section, the same problem characteristics that
argue for flex~bili~ and a~ptabili~r in ~utP' supervisory systems
also argue for allowing humans to participate ~ problem solving and
decision making processes. As the complexity of computer support for
the space station grows, the need for communication and shared
responsibility between the computer and space station resident= will
grow as well. If ever we reach the stage of a fully automated,
self-supporting space station, we are likely to first spend a
significant period of time in which computer assistants will provide
certain fu~ly-automated services (e.g., simply monitoring station
subsystems to watch for unexpected behavior), but will require
Interaction with their human counterparts in responding to many navel
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103
events. Effective methods for such man-machine interaction will
encourage the introduction of computer assistants for many more backs
than possible if totally auto meted operation were demanded. This
section considers some of the research issues related to developing
effective communication between AI systems and their users. Since
several other symposium participants will address the issue of
man-machine communication in general, I will try to focus this section
on issues specific to sharing problem solving responsibilities and to
transferring expertise from humans to their computer assistants.
Shared responsibility is a desirable characteristic whenever one is
faced with a multifaceted task for which humans are best suited to some
facets and machines to others. Humans ~ e mechanim=1 tools (e.g.,
wrenches) and computational tools (e.g., pocket cap curators) for
exactly such reasons. In the space station, we may find it desirable
to share responsibility On motor tasks, as in a human controlling the
mechanical robot arm in the space shuttle, in cognitive hanks, as in a
human an] computer system working jointly to troubleshoot a failed
power supply, or In perceptual tasks, In which a human may assist the
computer in finding corresponding pa Mets in multiple camera images so
that the co mput~r can then apply image analysis and enhancement
procedures to the images. In each case, shared responsibility makes
sense because the machine has certain advantages for some aspects of
the task (e.g., physical strength and the ability to operate in adverse
environments) while the human possesses advantages for other aspects
(e.g., motor skills and flexibility in dealing with the unanticipated).
Sharing in the process of problem solving also raises the prospects
for transfer of expertise. In many fields, humans learn a great deal
by acting as an apprentice to help a more advanced expert solve
problems. As the medical intern assists in various hospital
procedures, he acquires the expertise that event~1y alicws him to
solve the.same problems as the doctor to whom he has apprenticed. One
recent develcpment in AI is a groom g interest in constructing
interactive problem solv m g systems that assist in solving problems,
an] that attempt to acquire new expertise by observing and analyzing
the steps contributed by their users. This section argues that
research toward such ~ q apprentice systems is an important area
for NOVA support.
An Example
In order to ground the discussion of share J responsibility and
apprentice=, we briefly summarize a particular knowledge-based
consultant system designed to interact with its users to solve problems
in the design of digital circuits. This system, called LEAP (Mitchell
et al., 1985), is a prototype system which illustrates a number of
difficulties and opportunities associated with shared responsibility
for problem solacing.
LEAP helps to design digital circuits. Users begin a session by
entering the definition of some input/output function that they would
like a circuit to perform (e.g., multiply two numbers). LEAP provide=
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assistance in design mg the desired circuit, by utilizing a set of
if-then rules which relate desired functional characteristics to
classes of circuit implementations. For instance, one rule An this set
dictates that "IF the desired function requires converting an input
serial signal to an equivalent parallel si ~ , THEN one may use a
shift register." LEAP utilizes these rules to suggest plausible
refinements to the abstract circuit modules that characterize the
partial design at any given stage.
Figure 3 depicts the interface to IEAP as seen by the user. m e
large window on the right contains the circuit abstraction which is
presently being designed by the user/system. As shown An the figure,
the circuit consists at this point of two abstract circuit Locales.
For each of these circuit nodules, LEAP possesses ~ description of the
function to be implemented. At any point during the design, the user
selects one of the unimplemented circuit mc~ules to be considered, and
LEAP examines its rule set to deters me whether any rules apply to this
Techie (i.e., rules whose preconditions match to the specifications of
the circuit mc~ule). If LEAP determines that same of its rules apply
to this situation, it presents the raccc=en~ations associated with
these rules to the user. The user can then exam me these options,
select one if h_ wishes, and LEAP will refine the design accordingly.
Figure 4 depicts the result of such an implementation step. Should the
user decide that he does not want to follow the system's advice, but
instead wishes to design this portion of the circuit mantm1ly, he can
undo the rule-generate] refinement and use LEAP as a simple,
yraphics~riented, circuit editor.
lEAP provides a simple e~le of Abased Problem solving between man
and machine. me user dirts the focus of attention by selecting
With circuit He to refine new. [EAP suggests possible
impl ~ ntations of this McCabe, and Me user either approves the
raccrmen~ations or replaces them with his own. LEAP thus acts as an
apprentice for design. For design problems to which its rule base is
well-suited, it provides fur advice. For circuits completely
outside the scope of its knowledge it reduce= to a standard circuit
editing package, leaving the bulk of the work to the human user. As
the knowledge base of LEAP grows over time, one would expect it to
gradually take on an increasing share of the responsibility for solving
design problems.
LEAP also illustrates how such knowledge-based apprentices might
learn from their users (Mitchell et al., 1985) . In particular, LEAP
has a primitive capability to infer row rules of design by Ring
and generalizing on the design steps contributed by its users. In
those cases Are the user rejects the system's advice and designs the
circuit subrule himself, [EAP collects a training example of scone new
rule. That is, lamp r ~ rds the circuit function that was desir ad,
along with the user-supplied circuit for implementing that function.
LEAP can then analyze this circuit, verify that it correctly implements
the desired function, and formulate a generalized rule that will allow
it to raccmm£nd this circuit in similar subsequent situations. The key
to LEAP's ability to learn general rules from specific examples lies in
its starting knowledge of circuit operation. Although it may not
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105
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107
initially have the expertise to generate a particular implementation of
the desired function, it does have the ability to recognize, or verify,
the correctness of many of its users' solutions. on general, it is
easier to recognize a solution than to generate one. But once a
solution can be recognized and explained, then LEAP can generalize on
it by distinguishing that certain features of the example are critical
(those mentioned in the verification), whereas others are not (those
not mentioned On the verification).
LEAP is still a research prototype system, and has not yet been
subjected to testing on a large user ccmmunity. While there are no
dcNbt many technical issues still to be solved, it serves as a
suggestive example of how a knowledge-based consultant might be useful
as an apprentice even before its knowledge base has been fully
developed. It also suggests how its interaction with the user might
lead it to extend its knowledge base autcmatim=~1y. The methods for
collecting training example= and for formulating general rules appear
generic enough that similar learning apprentice systems might be
developed for many supervisory tasks of the kind discussed in the
previous section. Ocher current research is exploring the feasibility
of such learning apprentices in bask domains such as signal
Interpretation (Smith et al. 1985), proving mathematics theorems
(O'Rorke, 1984), and planning simple robot assembly steps (Segre and
DeJong, 1985~.
Nature of the Problem
The LEAP system suggests one kind of shared responsibility between
computer and human, as well as a mechanism for the gradual accretion of
knowledge by the system so that over time it can take on a
progressively greater share of responsibility for problem solving. The
ability to acquire new rules by generalizing from the users' actions
follows from LEAP's starting knowledge of how circuits work. That is,
it be-tins with enough knowledge of how circuits operate, that it is
_ _ _ ~ ,
~ . . . . . .
able to explain, or verify, the appropriateness of the users' actions
once it observes them. Once it has verified that the user's circuit
correctly implements the desired function, then it can generalize on
this action by retaining only those features of the specific situation
that are mentioned in this explanation. Similarly' if one tried to
construct such a learning apprentice for troubleshooting power supply
faults, one would want to include sufficient initial knowledge about
the power supply (i.e., its schematic) that the system could verify
(and thus generalize on) users' hypotheses about the causes of specific
power supply malfunctions.
~Thus, in order for a system to learn from observing its users, it
must begin with sufficient knowledge that it can justify what it
observes the user do. It seems that for supervisory tasks of the k md
discussed above' the primary knowledge required to construct such
explanations is a description of the structure and operation of the
system under supervision. Since AI has developed methods for
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representing such knowledge, supervisory tasks seem like good targets
for further research on learning apprentices.
In addition to cognitive tasks such as monitoring, designing, and
debugging, one might consider learn m g apprentices for robotics tasks
such as using tools (£OC Segre and DeJong, 1985 for one example).
Given a new tool for the robot to use, one way to train it Light be to
use a teleoperator to guide the robot through several uses of the
tool. For example, given a new type of fastener, a user might guide
the Robot to grasp the fastener and use it to fasten two objects
together. If the system could start with enough knowledge to explain
which features of its trajectory and other motions were relevant to
accomplishing the given Ok, then it ~ ght be able to generalize
accordingly. Research on such robotic learning apprenti~-c seems
worthwhile and highly relevant to the goals of the space station
program.
To understand the issues involved in sharing information and
responsibility between human and machine, it is instructive to consider
the issues involved in sharing responsibility strictly among humans.
In both cases there are certain subproblems that are best dealt with by
individual agents, and others where shared responsibility makes best
sense. Successful interaction requires arriving at an agreement on
which agent will perform which task. In LEAP, the user makes all such
choices. But ~ more complex scenarios the user may not want to spend
the time to approve every suggestion of the apprentice. In such case=,
there must be ways to agree upon a policy to determine which decisions
are worth having the human approve. Of course there are many other
issues that follow from this analogy as well: the cooperating agents
eventually need accurate models of their relative competence at various
subtasks. And there will be questions of social and legal
responsibilities for actions taken.
Here we have tried to suggest that one class of computer assistants
on the space station be viewed as Dynamic systems that interact with
their users and work toward extending their knowledge and competence at
the Cask they perform. Preliminary results f ~ AI suggest that this
is a worthwhile research task. m e nature of the space station
suggests that such self-refining systems are exactly what will be
needed. m e continually changing configuration of the station itself,
the continN ally changing crews and types of operations that will be
conducted aboard the space station, the evolving technology that will
be present, all dictate that the compute' assistants aboard must be
able to adjust to new problems, new procedures an] new problem solving
strategies over the life of the space station.
Research Rcco==en~ations
Here we suggest several areas In which NOVA fright support research
toward advanced interfaces for interaction bearer hens and
intelligent consultant systems.
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.
Ar~hitec~res that support graceful ~nsf~ of Wise and
r~nsibility. inseam tom de~relc ping hernia apprentice
sy~ for Apace station applications is arranged bash on
reaent AT rams and on me importance of such systems to Ache
Apace station pa ~ r am. A prudent rester ~ she ategy at this
point woNId be to support develcpment of a variety of learning
apprentices in various task areas (e.g., for troubleshooting
space station subsystems, for monitoring and controlling
subsystems, for managing robot manipulation of its
environment). Such a research strategy would lead to
experimenting with alternative software architectures for
learning apprentices, as well as an increased understanding of
the feasibility of constructing learning apprentices for
specific space station task areas.
Evolution of y~ainsize and initiative of interaction. As the
expertise of the apprentice grows, and as the human becomes more
familiar with the cc mpetence and communication capabilities of
the computer, one expects that the optimal style of
communication should shift. Changes may occur, for example, in
who takes the initiative in controlling the direction of problem
solving, and in the grainsize of the tasks (e.g., initially
small subtasks will be discussed, but later it may be sufficient
to focus only on larger gra m subtasks). Research on interfaces
that support these kinds of changes over time in the nature of
the interaction, and which support expect communion about
such issues, should be encouraged. Such flexible LntPrfa~=s are
important whether the apprentice learns or not, s lace the user
will certainly go through a learning period during which his
uniting of Me system's oompetence and foibles, and his
willir~ness to Must In the system drill change.
Task~rien~ced sties of cooperative problem solving. ~ order
~ understand the kinds of knowledge that must be car~unicat~d
during shared problem solving, it may be worthwhile to conduct
protocol shades in which a novice human apprentices winch an
expert to assist him and to ac~iire his e ~ rtise (e.g., at a
tack such as troubleshooting a piers of equipment). Data
collected from such experiments should provide a more precise
understanding of the types of knowledge ccmmunicated during
shared problem solving, and of the knowledge acquisition process
that the apprentice goes through.
Transferring knowledge from machine to man.
Given the plans for
a frequently changing crew, together with the likely task
specialization of cc mputer consultants, it is reasonable to
assume that in some cases the computer consultant will possess
more knowledge about a particular problem class than the human
that it serves. In such cases, we wcN1d like the system to
cammunicate its understanding of the problem to the interested
but novice user. Certa m work in AI has focused on using large
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knowledge bases as a basis for teaching expertise to humans
(e.g., Clancey and Letsinger, 1984~. Research advances on this
and other methods for communicating machine knowledge to humans
would place NASA in a better position for crew training and for
integrating intelligent machines into the human space station
environment.
SUMMARY
This paper presents a sampling of recommended research directions which
NASA may wish to support In order to accelerate the develcpment of AI
technology of particular relevance to the space station. We fr=l that
recent AT research indicates the potential for a broad range of
applications of AI to space station problems. In order for this
potential to become reality, significant support for heroic AI research
is needed.
Research toward developing a wide range of "hands-on' supervisory
systems for monitoring, controlling, troubleshooting and maintaining
space station subsystems is strongly reocmmYnled. Such research is
Important both because of its potential impact on reliability and
safety of the space station and because the technical development of
the field of AI is at a point where a push in this area may yield
significant technical advances. Such hands-on supervisory systems
could include both physically stationary supervisory systems that
mom tor electronic subsystems, power supplies, navigation subsystems
and the like, as well as physically mobile supervisors that monitor and
repair the exterior and inferior physical plant of the space station.
Important technical challenges remain to be addressed in both areas.
In support of developing and deploying such knowledge-based
supervisors, it is recam=ende] that research be conducted leading
toward interactive, self-extending knowledge-h~p~ systems. Such
systems may initially serve as useful apprentice= in monitoring and
problem solving, but should have a capability to acquire additional
knowledge through experience.
~ .,
m e evolutionary nature of the space
station together WlUn one turnover of crew assure that a continually
changing set of problems will confront onboarS computer systems. m is
feature of the space station, together with the need to continently
extend the knowledge of problem solvers onboarS, argue for the
importance of research toward interactive, self-extending knowledge
based systems.
There are certainly additional areas of AT research which would also
benefit the space station program. The goal of this paper is to point
out a few such areas, in the hope of stimnlating thought about these
and other possible uses of AI in the space station.
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AcKNowLEDGEMENTs
My thanks go to Allen Newell and Oren Etzion1 for providing useful
comments on earlier drafts of this paper. this work was supported in
part by NSF grant DCR-8351523.
NOTES
1. In fact, initial AI systems for troubleshooting and control have
generally been restricted to dealing with typed- m observation
inputs and to typing out their recommendations rather than exerting
direct control over the system. However, there are exceptions to
this, such as the YES/MVS system (Ennis et al., 1986) which directly
monitors and controls operations of a large computer system.
2. The research recommendations listed here represent solely the
opinion of the author, and should not necessarily be interpreted as
recommendations from the symposium as a whole.
3. LEAP also utilizes knowledge about behaviors of individual circuit
components, plum knowledge of how to symbolically simulate digital
~ e
Cl~lltS e
4. Other relevant knowledge includes the goals of the user (e.g., a
decision must be made to act within 15 seconds), and emp~ri~1 data
on the frequencies of various types of faults.
IN - -A
1984
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Davis, R.
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Representative terms from entire chapter:
artificial intelligence
