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robot being developed by the Defense Advanced Re-
search Projects Agency (DARPA). In this section, we
describe these two case studies and how we used
PRONTOE to simplify ontology development and
maintenance.
3.1 International Space Station
Our principle development activity for PRONTOE is
an ontology for the International Space Station (ISS).
We have worked for the past two years with NASA
flight controllers to develop and design an ontology
that partially models the ISS. The focus of the effort
is on planning for Extravehicular Activities (EVA),
basically space walks, so our ontological concentra-
tion is on ISS objects that are located on the outside
(or external to) ISS, such as power module and anten-
nas. PRONTOE, as mentioned in section 2.2, comes
with a base ontology, a domain base we call ISS-base,
and kernel extensions for EVA and for each flight dis-
cipline that supports a given EVA, such as electrical
power and motion-control systems. The users can
then use PRONTOE to extend these kernels, incre-
mentally as new ISS activities arise. To prepare for
an upcoming EVA, the flight controllers start with a
current configuration of the ontology, and use PRON-
TOE to develop and save a snapshot of the configu-
ration of equipment, power and control that is antici-
pated to be true at the time of the activity. In an exten-
sion to PRONTOE, we are developing a capability to
generate change forms concerning location and con-
figuration changes that resulted from the EVA to be
distributed to other ISS parties such as mass proper-
ties analysis teams and ISS guidance and navigation
teams.
We have developed interfaces that allow PRON-
TOE to automatically import from two large NASA
databases of ISS equipment: the External Configura-
tion Analysis and Tracking Tool (ExCATT) and the
Inventory Management System (IMS). The ontolo-
gies created by these systems are large, with 4855
axioms containing 283 classes and 897 individuals.
We have thus broken the ontology into kernels to ease
editing. By connecting to existing databases, we re-
duce the upfront editing time necessary to build the
ontology. We can also export to these databases,
so any changes that operators make using PRON-
TOE can be pushed back into the official databases
of record.
The end goal of PRONTOE is to have operators
add equipment to the ontology. As an example of edit-
ing in PRONTOE, we will walk through a user adding
a new type of gas tank assembly for the ISS. The ex-
isting gas tank assemblies are show in Figure 2 by se-
lecting the GasTankAssemblies class in the ontology
tree. The classes are all colored to mark their differ-
ent locations on ISS. The user will create a new class
by clicking on the “Add Class” button in the ontology
tree toolbar. The resultant dialog is show in Figure 3.
Inherited object and data properties that can be bound
are show in cyan. New properties for the class can
be added by clicking the appropriate plus button. In
this case, the user wanted to create a new class called
OxygenTankAssembly. Clicking OK on the dialog
creates the appropriate axioms and marks the ontol-
ogy as dirty. In Figure 4, the user is in the process of
creating and specifying a new individual of Oxygen-
TankAssembly. Object properties are specified in the
top table and data properties specified in the bottom
table. If object or data property hasn’t been specified,
we mark the field yellow.
The first user trials of PRONTOE involved EVA
flight controllers, and to a lesser extent the robotics
flight controllers (known as ROBOs). The EVA flight
controllers generally approved of our current develop-
ment, but asked if we might build a tighter interface
to the 3D graphics engine they use known as DOUG
(Dynamic Onboard Ubiquitous Graphics). But they
also indicated that knowledge of how EVA serviced
equipment was related to the information in the oth-
ers kernels, e.g., power and computer control, would
be useful to them for setting up preconditions on their
EVA tasks. Our ROBO flight controller was skeptical
that the ROBO team would use PRONTOE for proce-
dures, but she saw a number of potential uses for the
tool, such as providing support for operational plan-
ning meetings, for collaboration among disciplines,
for troubleshooting training, and for use in simulation
scripting meetings.
Later in the project, we demonstrated our cur-
rent version of PRONTOE to core systems flight con-
trollers. They all were impressed with how we had
pulled together data from disparate sources into one
integrated view and suggested that we have a series
of one-on-one knowledge engineering meetings with
each of them to see if our kernels had enough key con-
cepts modeled for the users to extend them without
our help. We began those sessions with the vehicle
motion control flight controller who spent an after-
noon with us investigating the ontology and pointing
out what was missing if he were to use it in his day-to-
day operations. The resulting additions included be-
ing able to model internal items that connect to exter-
nal items, allowing multiple remote power controller
modules (RPCMs) in our power channel models, and
adding computer control channels to augment our re-
lations, controllerFor and controlledBy.
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