textEffective plan specification, solution extraction and plan execution are critical to
the ability of single- and multi-agent systems to operate given the dynamism and
uncertainties that exist in real-world domains. While much traditional planning
research has focused on increasing the tractable search space size for fast and
efficient planning, the real-world application of domain-independent planners has
suffered due to a lack of corresponding advances with respect to new domain
representations, replanning in dynamic and uncertain environments and metric
planning. This research implements the Metric Model Checker (MMC) to extend
and enhance planning capabilities by applying symbolic model-checking (SMC)
techniques for planning in causally specified domains using the causal language,
C. SMC is a formal verification technique for propositional satisfiability used to
test semantic properties (e.g. Computational Tree Logic) of Boolean domains. C
is an action language based on a theory of causal explanation of action effects that
supports action preconditions, indirect effects, non-determinism and non-inertial
fluents. MMC provides five main capabilities: i) new domain descriptions –
MMC translates and compiles C domain descriptions into a format that supports
the application of model checking toolkits; ii) extended-goal representations –
MMC supports incremental plan generation for multiple, simultaneous
temporally-extended goals; iii) replanning – MMC uses BDD representations of
generated plan search information to provide intelligent replanning and learning
during plan execution in a manner that supports the responsive capabilities of
recurrent replanning while providing the goal achievement guarantees gained
from precursor methodologies, and iv) metric planning – MMC integrates a linear
programming toolkit with its Boolean model-checking algorithms to provide
support for plan generation in certain metric (integer-valued) domains while
mitigating the state explosion problem; and v) distributed operation – MMC is
implemented in a CORBA distributed environment that allows it to be rapidly
integrated with existing agent-based systems and supports interaction with
external environmental simulators.Electrical and Computer Engineerin