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pabilities. We proposed two approaches with the
aim to generate only plans which are also moni-
torable. The first approach enhances action specifica-
tions with monitoring preconditions. The second ap-
proach, which provides more flexibility, is to dynam-
ically determine the monitorability of entire plans.
We have also proposed to integrate the monitorabil-
ity checks into the planning algorithm. Such an in-
terleaved approach achieves that plans which are not
monitorable are already discarded at an early stage
during the planning process.
So far, the issue of utilizing diagnostic results
in high-level planning in autonomous systems has
gained little attention among researchers. In par-
ticular, we are not aware of any work from other
researchers which has addressed the issue of AI-
planning with degraded software capabilities in au-
tonomous systems. The Remote Agent architecture
(Williams et al., 1998) employs model-based diag-
nosis methods for the detection and localization of
hardware failures. If such a failure cannot be handled
locally, the degraded capabilities are reported to the
planning system and replanning is performed. How-
ever, the scope of that work is quite different from
ours: it deals with hardware rather than software, and
it does not specifically address the modelling of capa-
bilities or plan monitoring issues.
Model-based diagnosis techniques can also be em-
ployed for the execution monitoring of plans, see,
e.g., (Roos and Witteveen, 2005). The authors of (Mi-
calizio and Torasso, 2007) use model-based diagno-
sis techniques to monitor the execution of multi-agent
plans. One aim of this work is to provide the global
planner/scheduler with the assessed status of robots
and the explanations of failures. However, a deeper
discussion of the planning and plan monitoring issues
in this context is not provided.
The practical applicability of our approach to ex-
isting robot control systems is limited by the fact that
most components in such systems are vital, i.e., when
a vital component fails, then the robot is not able to
operate anyway. In our examplesystem in Fig. 1, only
3 out of 9 componentsare non-vital, namely BaD, Son
and Kic. This indicates that the architectural design
of robot control systems should accomodate runtime
reconfiguration; in particular, the number of compo-
nents should be higher, and non-vital functionality
should be encapsulated in separate components in or-
der to achieve that there are many components with
non-vital functionality.
An interesting direction for future research is the
question of how to extend our approach to hard-
ware failures. The modelling of hardware capabilities
could be challenging, in particular because hardware
components may degrade gradually. E.g., the driving
unit may no longer be able to perform specific move-
ments after the breakdown of a single wheel.
Another assumption behind our work is that the
selected set of leading diagnoses comprises the real
fault, hence removing all components in those diag-
noses switches the system to a safe state where all
remaining components work correctly. This may not
be the case in practice, thus a careful selection of the
leading diagnoses is necessary.
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