Unifying Model-based and Reactive Programming in a Model-based
Brian Williams, Vineet Gupta.
Real-time model-based deduction has recently emerged as a vital component
in AI's tool box for developing highly autonomous reactive systems. Yet
one of the current hurdles towards developing model-based reactive systems
is the number of methods simultaneously employed, and their corresponding
melange of programming and modeling languages. This paper offers an
important step towards unification of reactive and model-based
programming, providing the capability to monitor mixed
hardware/software systems. We introduce RMPL, a rich
modeling language that combines probabilistic, constraint-based modeling
with reactive programming constructs, while offering a simple semantics in
terms of hidden state Markov processes. We introduce probabilistic,
hierarchical constraint automata, which allow Markov processes to
be expressed in a compact representation that preserves the modularity of
RMPL programs. Finally, a model-based executive, called RBurton is
described that exploits this compact encoding to perform efficent
simulation, belief state update and control sequence generation.
author= "Brian Willams and Vineet Gupta",
title= "Unifying Model-based and Reactive Programming in a Model-based
booktitle= "Proceedings of the 10th International Workshop on
Principles of Diagnosis"
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