  In this paper is presented a framework for treating uncertainty in optimal
decision problems occuring in combat situations, in order to robustly select
the optimal strategy. A stochastic version of the popular Lanchester's
aimed-fire model is considered as the underlying combat system describing the
combet dynamics, and upon this an optimal decision rule for allocating forces
is constructed. This approach results to a very extendable optimal decision
framework, where the optimal strategy is chosen by simultaneously treating
robustly uncertainty regarding critical combat parameters.
