  A symbolic approach to decentralized set-valued state estimation and
prediction for systems that admit a hybrid state machine representations is
proposed. The decentralized computational scheme represents a conj unction of a
finite number of distributed state machines, which are specified by an
appropriate decomposition of the external signal space. It aims at a
distribution of computational tasks into smaller ones, allocated to individual
distributed state machines, leading to a potentially significant reduction in
the overall space/time computational complexity. We show that, in general, such
a scheme outerapproximates the state set estimates and predictions of the
original monolithic state machine. By utilizing structural properties of the
transition relation of the latter, in a next step, we propose constructive
decomposition algorithms for a recovery of the exact state set outcomes.
