Download Expert Systems for Space Station Automation

Expert Systems for Space Station
Automation
Michael P. Georgeff and Oscar Firschein
ABSTRACT: There will be a permanently
manned,fullyoperationalspacestation
by
themid-1990s.Automation
of thestation
will increase space station autonomy and
utilizethecrewmoreeffectively.Artificial
intelligencetechnology(robotics,image
perception,expertsystems,andplanning
systems)willplayanimportantrole.
This
paper describes expert systems and indicates
the useof these systems in space station automation. We first discuss the nature of expert
systems,typicalapplications,andlimitations. The role of expert systems in space
stationautomationisthenindicated,and
thenecessaryresearchanddevelopment
is described.
Introduction
The National Aeronautics and Space Administration (NASA) has begun a program
thatwillresultinapermanentlymanned,
fully operational United States space station
by themid-1990s.Thespacestation
is intended to support scientific and commercial
endeavors in space, stimulate new technologies, enhance space-based operational capabilities, and, in general, maintain America’s
leadership in space during the last decade of
this century and into the next century.
The
role of the space station in a national space
infrastructure is shown in Fig. 1. Note that
the station can be used as a base
by orbital
maneuveringvehiclesandorbitaltransfer
vehicles (OMV and OTV). The
OMV is a
remotely controlled vehicle that can be controlled from several hundred miles away
by a
crew member on the shuttle or station. The
OTV would extend man’s presence far beyond the range of theOMV by being able to
travel tens of thousands of miles from the
station to geosynchronous orbit and then to
the moon, transporting large platforms, or a
crew of two to four astronauts, or an OMV.
Automation of the space station
is required
formoreeffectiveuse
of thecrew and to
make the station more autonomous. In a reThis wrork was carried out under NASA Contract
NASZ-11864;the project monitor was Dr. Henry
Lum, NASA Arnes Research Center. The authors
are with the Artificial Intelligence Laboratoxy of
SRI International, 333 Ravenswood Ave., Menlo
Park, CA 94025.
November 1985
which the module can
be invoked; the second
centstudy [I], SRI Internationalexamined
is the functional part to be executed whenautomationconceptspostulatedbyfour
ever the module is invoked.
NASA contractors (TRW, Martin Marietta,
The other important feature of such sysGE,andHughes) [2]-[5] to determine the
temsisthat,sincetheyareusuallynonrequiredresearchanddevelopment
in artideterministic,alargenumberofmodules
ficial intelligence(AI)toattaintheseconmay be “applicable” (candidates for activacepts. The goals of the SRI study were to
(1) provide guidance with respect to the state tion) at any given moment.Thus, it isnecessary tospeclfyacriterionthatdetermines
of the art in AI-based technologies, (2) rehow to select which of the applicable modview the results of the four NASA “concept
ulestoexecutenext,andwhattodoafter
design” contractorsto determine theAI capaselection. Some system select a single modbilities required by the designs, (3) delineate
uletoexecute
anduse“backtracking”to
a series of demonstrations that would indiallow other choices to be made if the f i t
cate the existence of these capabilities, and
choice is not a fruitful one. Other systems
(4) developan R&D planleadingtosuch
applymodulesinparallel,andyetothers
demonstrations. The present paper, derived
make irrevocable choices.
from the SRI study, provides an introduction
Developmentof“expert-system”lanto expert systems and indicates the applicaguagesisbetterregardedasanareaof
tion of such systems tothe space station. The
SRI final report describes the demonstrations programmingmethodologyorsoftware
engineering and, indeed, has made a signifiand R&D needed to attain the expert system
cant contribution to these fields. However,it
technology for the evolving space station.
is veryimportanttorealizethatsuchlanguages canbe used for avariety of programming tasks apart from the construction
Expert Systems
of systemsthatemulateexpertreasoning.
Theterm expert systems wasoriginally
Consequently, it ismisleadingtocallany
used to denote systems that utilize a signifisystem developed in this manner “an expert
cant amount of expert information about a
system.”Nevertheless,influenced
by the
particular domain to solve problems in that
considerableweightofacceptedusage,
domain.Becauseoftheimportantrole
of
we shall continue to call such languages (toknowledge in such systems, they have also
getherwiththeirsupportingenvironments)
been calledknowledge-based systems. Howexpert-system programmingtools.
ever, the term has since been applied
to so
The second use of the term expert system
many diverse systems that its original meanistodescribeanysystemthat“reasons”
ing has been largely lost. There are essenabout a problem in much the same way hutially two uses of the term that need to be
mans do. Some of the features distinguishing
differentiated.
these systems from standard application proFirst, the termis often used to describe any grams follow.
systemconstructedwithspecialkinds
of
“expert-system” programming languages and
Knowledge
tools.Theseincludeproductionsystems,
Each contains a data base of knowledge
rule-based systems,frame-basedsystems,
(usually in a specialized area) represented in
“blackboard”architectures,andtheProlog
arelativelynaturalformthatallowssome
language. In conventional programming lansortofreasoningtobecarriedout.The
guages, the control of program activities is
knowledge representations are usually symdetermined by the sequence of instructions.
Unlike standard programming languages, ex- bolic,reflectingthequalitativenatureof
much human reasoning. Early expert systems
pertsystemshavevery
little controlstrucused relatively simple rule-based or frameture: functional components or modules are
based schemes for representing this knowlinvoked (activated) primarily on the basis of
edge.Recentsystemshaveaugmented
propertiesofthecurrentsituation.Thus,
each module usually consists of two compo- theseapproachesbymoresophisticated
knowledge-representation formalisms.
nents: the first describes those situations in
0272-1708/85/1100-~3/$1.000 1985
3
\
P
I
ORBITER
Fig. 1. Elements of anationalspaceinfrastructure.
Extensibility
The representation of knowledge is such
thatmodificationsof
oradditionstothe
knowledgebasedonotrequireextensive
modification of the entire system. Thus, the
systems are extensible, degrade “gracefully”
rather than catastrophically as elements are
removed, and can evolve without extensive
rewriting. This requires highly modular systems, in which the semantics of each module
can be specified independently of other modules. Such evolutionary capability is essential for space station automation.
Flexibility
The systems are often highly reactive; that
is, the choiceof actions to be performed next
by the system depends primarily on significant features of the current situation, rather
than onthefixedandimmutablecontrol
structurethatcharacterizesmorestandard
software systems. This is particularly important for space station applications for which
thecontrollingsystemsmustbeflexible
4
enoughtorespondrapidlytoenvironmental changes.
Explanation
“team” consisting of a programmer and an
expertinthefield.Theprogrammerhas
come to be known as a knowledge engineer
because he must be familiar with the knowledge-representationschemeusedinthe
system.
Manysystemscanretracethereasoning
sequenceemployedandexplainwhatwas
done at each step and why. This explanatory
capability enables the userto accept or reject
State of the Art in Expert-System
the system’s conclusionsif he disagrees with
Programming Tools
itsreasoningandaidstheexpertindeThere currently exist a number of expertbuggingthesystem. The usefulness of the
explanatory system derives from the fact that systemprogrammingtools.Theseinclude
OPS [6], S1 (Teknowledge),ART(Inferthe reasoning performed reflects the user’s
ence Corp.), ROSIE (Rand Corp.), and KEE
own reasoning processes.
(Intellicorp). All the available “off-the-shelf’
Incomplete or Inexact Data
systemsare little morethanprogramming
languages as discussed above -namely,
Many of these systems can carry out reasoningprocessesonincomplete,uncertain,
theyprovide a programminglanguage
or inaccurate data. For example, the effects
(usuallywithaveryrichsupportenvironof a given action may be incomplete, the con-ment)that is suited to constructingexpert
clusion of a diagnosis may be uncertain, or
systems. However,just as in the caseof conthere may be errors in sensory information.
ventionalprogramminglanguagesapplied
At present, expert systems do not acquire
to a complex problem, there is much work in
theirexpertisethroughexperience,
but are
constructingasophisticatedexpertsystem
rathergiventheneededinformation
by a
with an expert-system programming tool.
IEEE Control Systems Magazine
Whiletheavailableexpert-systemprogramming tools are well suitedto developing
expertsystemsrequiringrelativelysimple
knowledge representations, they may not be
capable of handling the more powerful and
expressive knowledge formalisms needed for
morecomplexproblemdomains.Indeed,
theycanactually
hinderdevelopmentin
these areas because the user maytry to warp
the problemto better fit the tool.It is usually
better to build the more complex formalisms
upon amorebasicprogramminglanguage
suchasLISPandProlog.Bothlanguages
serve different needs,and it is likely that any
reasonably sophisticated system will haveto
use both. In particular, Prolog is useful for
representing knowledge that is naturally expressed as a set of facts and a set
of rules,
withtherulesserving
to definehownew
facts are to be deduced from what is already
known.LISP is wellsuitedtoprocedural
programmingandtoimplementingmore
complexknowledgerepresentationsinthe
form of list or network structures.
State of the Art
in Expert Reasoning Systems
The general categoriesof tasks that expert
systems have been applied to can be broken
down as follows.
Interpretation and Diagnosis
This category of expert systems includes
all those that can accept data from the user
about a particular case and, when sufficient
information has been received, return a diagnosis or interpretation of that case. Examples
includemass-spectrometerdatainterpretation (DENDRAL [7]) and medical expert
systems(EXPERT [8], MYCIN [91). Systems for fault diagnosis and isolation
also fall
into this class. Some current systems under
development are listed in the Table.
Design Systems
Theseareexpertsystemsthatmaybe
given particular information and constraints
and are required to produce an output that
satisfiesthegivendesigncriteria.
An example is XCON [ 101, an expert system that
designs computer configurations.
Prediction and Induction Systems
Thesesystemsacceptdataandlook
for
patterns or other forms of order. When such
patternsarefound,theycan
be combined
with information about a particular case
to
predict the most likely outcome.An example
of an inductive system is INDUCE, which
inferstherelationshipbetweensymptoms
and disease in soybeans.
November 1985
Monitoring and Control Systems
Thesesystemsreceivespecificon-line
data from sensors regardingthe object being
monitored and/or controlled. These data are
rapidly interpreted by the expert system and
theappropriateresponsesgenerated.Ina
monitoring expert system, specified alarms
are triggered whenever particular
critical situations are detected. REACTOR, a nuclearreactor-monitoringsystem,andVM,
a
patient-monitoringsystem- for hospitalintensive care wards, are examplesof this type
of expert system. YES/MVS, an IBM system,is an exampleofareal-timeexpert
system used to control a computer operating
system. Often, the generation of an appropriate response will require simulation of the
expected effects of possible actions
on the
controlled system.
Any prematureenthusiasmovertheapparent success of these systems needs to be
tempered by thefollowingobservations.
First, very few such systems have been developedbeyond the experimentaltesting
stage.Althoughsuchtesting is essentialin
establishing the soundness of the basic design,serioustechnicalproblemscanstill
arise in getting the system to work in a real
environment.
Second, mostof the expert systems developed to date cannot easily be generalized to
handle problem domains other than the ones
they were specifically designedfor. Thus, an
expert system can be considered analogous
to
an idiot savantwho can deal very effectively
with a specialized field butis incompetent to
deal with topics outside its purview.
Third, the kinds of knowledge that existing systems can represent are relatively simple.Thisdoesnotmeanthattheyare
not
useful, but it does mean that the application
of expert systems to more complex domains
will require a significant amount of research
in knowledge representation.
Present Limitations
of Expert Systems
There are limitations in present-day expert
systems because of the following technical
problems.
Knowledge Representation
It is difficult to develop representationsfor
specificdomainsthatarecomputationally
tractable and still capture the important characteristics of the domains. In particular, formalisms to representtime,space,actions,
processes, mechanisms, and other complex
objectsneedtobedeveloped(e.g.,[MI).
Whileconsiderableattentionhasbeen
focused on static problemdomains,and
on
capturing an expert’s knowledge inthe form
of heuristic rules of thumb, relatively little
attentionhasbeenapplied
to dynamicdomainswheremuchexpertknowledgeis
procedural-that is, whereexpertknowledge involves reasoning about sequences of
tests and actions. Another problem is how to
Table
Typical Expert Maintenance Systems
EL, an MIT program, simulates the operationof an electrical circuitand deduces the
possible cause of a failure [ll].
0 IN-ATE, Navy Center for Applied Research in Artificial Intelligence, is an expert
[ 121.
system for guiding a novice technician in troubleshooting electronic equipment
0 MDIS (Maintenance-and-Diagnostics Information System), Boeing Aerospace, is an
expert system for maintenance and diagnosis [ 131.
0 IMA (Intelligent Maintenance Aid), General Dynamics,
is a prototype expert system
fordiagnosisofthemicrowavestimulusinterface(MSI)
of theF-16Avionics
Intermediate Shop [ 141.
0 DART, an ongoing, joint, IBM-Stanford University project, uses a causal model of
a computer for fault diagnosis.
0 DELTA, or CATS-1, is an expert system developed
at GE for troubleshooting dieselelectric locomotives [ 151.
0 ACE (Automated Cable Expertise), Bell Laboratories, identifies trouble
spots on the
basis of data from trouble reports and suggests the repairs to be made [ 161.
0 LES (Lockheed Expert System), developed by the Lockheed Palo Alto Research
Laboratory, is a general-purpose expert system that has been applied to diagnosing
faults in a complex switching network.
0 PES (Procedural Expert System), developed
at the AI Center of SRI, is a system
for space station maintenance that explicitly represents procedural knowledge while
retaining the benefits of traditional expert systems [17].
0
5
represent “commonsense” knowledge -the
type of knowledge a person uses in dealing
withtheworld.Unlesssuchknowledge
is
incorporatedintoexpertsystems,theywill
remain “fragile,” Le., unable to function exceptwhendealingexclusivelywithintheir
narrow specialties.
representation, then formingit into an explanation that is acceptable to the user.
Use of Metaknowledge
The system should have knowledge about
the knowledge it contains, and be able to use
suchknowledgein
its reasoningstrategy,
Such metaknowledge becomes crucial when
Reasoning
large knowledge bases are to be used, since
Thereasoningsystem
mustbeable
to
otherwise too much time is expended on unreach conclusions on the basis
of information
productivesearches.inappropriateactions,
about the current situation and the knowledge and needless data requests.
contained in the knowledge base. Much work
Learning Capabilic
is required to develop techniques for qualiCurrently, the designer of the system, not
tative and quantitative reasoning. Techniques
the system itself, learns by experience as the
arerequiredforreasoning
on thebasisof
system is used. Thus, the designer, not the
uncertaininformationandweaklysupsystem, modifies the knowledge base. It is a
ported implications, for updating the knowlnontrivial taskto determine which rules need
edge base over time, and for maintaining its
modification when the expert system is not
consistency.
performing up to an expert’s standards. The
Knowledge Acquisition
designermustconsultwiththehumanexThere is amajorproblem
in obtaining,
perts to determine how the rules have to be
representing, and debugging expert knowlmodified or augmented.Thesystem
itself
edge about a particular domain. Even for the has no way of determining that the user is
best-understoodproblems,typicallyabout
dissatisfied: nor of automatically correcting
five man-years of effort are required to dethe source of the difficulty. It is important
velop a large system that begins be
to robust.
thattechniques be developedforacquiring
Methods are now being developed for dealknowledge automatically as the system pering with the knowledge acquisition problem, forms its tasks.
and these methods should reduce the time it
Space Station Applications
takes to build new systems.
Some
application areas of expert systems
Verification
in the space station follow.
Since the system maybe inconsistent inits
Maintenance and Repair
knowledge or rules,itisimportantthat
manual and eventually automatic verification
Expert systems will be important in subtechniques be developed. One approach is
system and satellite servicing for carrying out
toprovideuserswithknowledge-based
routine tests, noting possible deviance, and
debuggingtoolsthat,forexample,check
flagging abnormal transient operation before
forinconsistenciesandgapsintheknowla hard failure occurs.In addition, expert sysedge base and help the experts and knowltems will be needed to isolate and diagnose
edgeengineers to communicatewithone
faults and toindicatemethods of handling
another [ 191. However, the problem of formalfunctions.
mallyverifying a knowledgebaseconExpert Process Controller
structedwithanexpressiverepresentation
scheme is, in general,intractable.ConsidIn manufacturing processes carried out on
erableresearchwilltherefore
be necessary
the space station, expert systems are required
toextendthelimits
of currentverification
forqualityassurance(interpretingprocess
techniques.
deficiencies),processcontrol(suggesting
processingcorrectionstoattainbetterreExplanation Capabilities
sults), and equipment maintenance (isolating
The explanations produced by current exequipmentfaultsandinitiatingcorrective
pertsystemsareusuallyindications
of the
action).
solution path traversed before attaining the
Subsystem Monitoring and Control
present status. However. what the user often
desiresis acausalexplanationbasedon
Expertsystemscan
be appliedtosubphysical reasoning. This type of explanation
systems,such as thepowersubsystem,
to
must be based on a very rich description of
monitor and control complex operations and
the problem domain, for which a representamake difficult decisions. Maintenance
of lifetion of the model or mechanism underlying
support systems, operation and servicing of
the reasoning is normally essential. Furtherexperiments,onboardmissioncontrol,and
more, there is difficulty in making explicit
automation of traffic control could also be
the information that may be implicit in the
handled by expert systems.
6
intelligent Autonomous Robots
An expert system could guide the scheduling of theconstructionandassemblyof
large space structures, the servicing of satellites, deployment of payloads, OMV/OTV
operations, and the transfer of cryogenic
fluids. Eventually, as effector and sensor capabilities are developed, these processes could
be automated and handledin their entirety by
autonomous robots.
Astronaut’s Associate
An expert system could actas an astronaut’
advisor to aid in the use of a complex program or a complicated item of equipment.
The advisor could suggest parameter values,
the meaningof certain system responses, and
sequences of control actions.
There has been considerable interest in expert systems within theNASA centers. Some
of the expert systems being developed are:
T h e LOX E x p e r t S y s t e m [20],a
knowledge-based system being applied to
asemi-real-timeapplicationmonitoring
the loading of cryogenic fuel for the space
shuttle.The KNOBS constraint-and
frame-orientedexpertsystemisbeing
used.
FAlTH (Forming and Intelligently Testing
Hypotheses)isa
JPL systemforautomating the Voyager down-link process.
PES, a procedural expert systemfor space
station maintenance,is being supportedby
NASA Ames Research Center [17], [21].
h addition, several NASA centers are in
the process of developing expert systems for
various applications, including fault diagnosis of the life-support system and distribution
of electric power. Most of these applications
utilize the results of expert-systems research
conducted at universitiesduringthepast
decade and perform relatively simple
tasks
that can be achieved with relatively simple
knowledge representations.
In many of theseareas, there will be some
subclasses of problemsthatcanbesolved
by constructingsimpleexpertsystemsthat
use relatively elementary knowledge-representationschemes.Commerciallyavailable
expert-systemprogrammingtoolsmaybe
adequate for creating such systems, while the
more complex problems of some applications
would at least be indicated as targets for future resolution. Furthermore, there are some
applications, such as simple monitoring and
control,forwhichcurrenttoolscouldbe
used advantageously, eventhough theresulting systems might not reflect any expert
reasoningat all orprovideanyusefulexplanatory capabilities.
However, the more complex space station
tasks require expert systems capable of sol € € € Control Systems Magazine
0 Same as the preceding, but using distribreasoning. Unless this is done, it is difficult
phisticated reasoning about actions, events,
utedexpert-systemarchitecturewiththe
toseeanypossibilityofautomatingspace
and processes. Typicalof the kind of knowlaim of improving- real-time performance
stationfunctions;furthermore,expertsysedge used in these applications are the maland evolutionary potential.
tems will only find
useful application in a
function handling procedures for the space
few relatively simple t a s k s .
shuttle (STS). The procedures are extremely
0 A system capableof fault isolation of mulcomplex, and involve performing sequences
tipleinteractingsubsystems,using
stanof actions and tests that change the state of
dard maintenance procedures.
the space shuttle and its environment. FurDemonstrations
Same as the preceding, but operating unthermore,thenatureof
this knowledgeis
The following demonstrations would verder real-time constraints and allowing for
procedural-that is, it is represented in the
data errors.
ify that expert-system capabilities are availform of complex procedures for achieving
able for maintenance and repair, controlling
Asystem for control of a single manugiven goals rather than as a set
of “rules”
manufacturingprocesses,andsubsystem
facturing process or a single experiment.
about shuttle operations.
monitoringandcontrol.ThesedemonstraAschematicviewofanexpertsystem
0 A spaceborne processor particularly suited
tions can start withgroundoperationsand
suited to advanced applications is shown in
to mechanization of expert systems.
demonstrations; they would next proceed to
inFig. 2. The centralroleofreasoning,
actual spaceflight implementation- first on
volving a knowledge base and a reasoner and
the shuttle, then on the space station itself.
Medium-tem (1993-2000):
planner, is indicated. The reasoning portion
Near-term (1985-1992):
receivesinformationabouttheworldboth
An expert system capable of solving probfrom the system interfaces (communication)
lems in an isolated subsystem when some
0 Information retrieval from a data base that
and fromthesensors(controlkensing)and
substeps of a standard maintenance procedescribes the structure and functionalityof
integratesitwiththeinformationinthe
dure
are inapplicable.
majorsystemsinformal
or semiformal
knowledge base using the consistencymain0 An expert system capable of fault diagnolanguage.
tainer. The roleof the consistency maintainer
sis and recovery in an isolated subsystem
is to ensure that changes to knowledge-based 0 Informationretrievalfromadatabase
when
a major portion
of some standard
describing maintenance and operating proentries do not cause inconsistencies. The outmaintenance procedure is inapplicable.
cedures
of
major
systems
in
formal
or
put of the reasoning portion is used to comsemiformal language, including informaSame asthe preceding, but involving mulmunicate with other systems and to generate
tion
tiple
interacting subsystems.
as
to
the
purpose
of
the
procedures
commands to the effectors and sensors.
and
their
component
steps.
Automatic
verificationtechniquesfor
The developmentof such systems can only
guaranteeingthatanexpertsystemis
0 A system capable of fault isolation of a
be achieved by pursuing a well-focused re“safe,” Le., cannot harm the subsystems
single subsystem, using standard maintesearch plan investigating the critical issues
that it controls.
nance procedures.
involvedinknowledgerepresentationand
1
f
t
CONTROL~ENSING
REASONING
COMMUNICATION
-
Data
Input
Consistency
Maintainer
~
Monitor
f
rc
Knowledge Base
e Dynamic World Model
e CAD/CAM Data Base
System Structure
e Operational Procedures
0 Meta-level Knowledge
Kl
.faces
L
Goals
-
Data
Output
+
.
Controllers
(Effectors)
,
11
plans
(In tent ions)
(Tasks)
\
‘-
SS Subsystems
or Equipment
\
/
Sensors
(Perceptors)
!*-
Planner
/
Command Reasoner and
Generator
L
Fig. 2. Expertsystem for the space station.
November 1985
7
An expertsystemforuseinmanufacturing, capable of limited quality control,
productioncontrol,andmaintenance
and faultdiagnosis of amanufacturing
process.
0
Long-term (2001-2010):
0
0
0
An advanced expert system that can run
manymajorsubsystems,maintainand
controlexperimentsandmanufacturing
processes,scheduletasks,andinteract
with intelligent robots.
An advanced expert system that can cope
with an unanticipated major system failure
(liketheonethatoccurredduring
Apollo 13).
An expert system that can improveits own
maintenance skills -i.e., "learn" from
experience.
Conclusions
In examining space station applications, it
is evident that a high return on research investment,intermsofsafety
and effective
utilization of ground and spacecraft crew, is
to be found in automation of the operation,
maintenance,andcontrol
of spacestation
subsystemsandmanufacturingprocesses.
Thecrucialcharacteristicoftheseapplications is that the domain is dynamic- Le., it
involvesreasoningabouttheeffects
of sequences of actions and tests that can change
the state of the world over time. Moreover,
because various subsystems willbe operating
simultaneously, it is important that the representation be sufficiently rich to enable reasoningaboutconcurrencyandsubsystem
interaction and that efficient procedures for
automaticschedulingandsynchronization
be developed.
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F. D. Miller.andD.H.Copp,"ACE:An
Expert Systemfor Telephone Cable Maintenance." Proceedings of the8rhInternarional JointConferenceonArtificial
Intelligence. William Kaufman. Inc..
95 First Street. Los Altos, CA; Karlsruhe,
WestGermany.Aug.1983.
M.Georgeffand U. Bonollo. "Procedural
Expert Systems," Proceedings of rhe Eighth
Inrernational Joinr Conference on Artificial
Inrelligence. Karlsruhe.WestGermany.
1983.
[le] ArtificialInrelligence. 1984, vol. 24,(1-3).
Specialvolume on qualitativereasoning
about physical systems.
[19] B. G. Buchananand E.H.Shortliffe,
"Completeness and Consistency in a RuleBasedSystem,"inChapter
8. AddisonW e s l e ~Series in ArtificialIntelligence.
Rule-Based Expert S)stems. Reading, MA:
Addison-Wesley,1984.
[20] E. A.Scarl,J.Jamieson.and
C. Delaune,
"Knowledge-BasedFaultMonitoring
and
Diagnosis in Space Shuttle Propellant Loading."(InterimReport),Cambridge,
MA,
MITRE,1984.
[21] M.P. Georgeff, "AnExpertSystem
for
Representing Procedural Knowledge,"Joint
Services Workshop on Artificial Intelligence
in Mainrenance. Volume I : Proceedings,
AirForceHumanResourcesLaboratory,
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Michael P. Georgeff is
theProgramDirector,
RepresentationandReasoningGroup.Artificial
IntelligenceCenter, SRI
International.He is the
PrincipalInvestigator for
theNASA-sponsored
study of PES, a procedural
expert system for diagnosis of faults in spacecraft
systems, and was a member ofthe SRI teamthat
studied space station automation.Dr.Georgeffhasa
B.Sc. inphysics
andmathematicsfromMelbourne(Australia),a
B.E. from Sidney (Australia). and the D.I.C. and
Ph.D. from Imperial College (London). His major
interestsareinplansynthesisandlogics,expert
systems,andprogramminglanguages.Heisa
member of theAssociation for ComputingMachinery,theAmericanAssociation for Artificial
Intelligence, and the Australian Computer Society.
Oscar Firschein is a Staff
Scientistandmemberof
thePerception Group at
theArtificialIntelligence
Center, SRI International.
HewasthePrincipal
Investigatorforthe SRI
Space Station Automation
study. Prior to coming to
SRI.hewas a Senior
Member of the Lockheed
Palo Alto Research Laboratoryperforming research in image analysis. He has a B.E.E. degree
from the City College of New York and an M.S.
degree in Applied Mathematics from the University of Pittsburgh. His interests are in the use of
artificial intelligence techniques for applied problems, and in image analysis. He is a Senior Member of the IEEE, a member of the Association for
Computing Machinery, the American Association
for Artificial Intelligence, the Pattern Recognition
Society. and the IEEE Computer Society.
IEEE Control Systems Magazine