Merging `reasoning´ and filtering in a Bayesian framework-some sensitivity and optimality aspects

It is shown how to incorporate symbolic or logical knowledge into a conventional framework of noisy observations in dynamical systems. The idea is based on approximating the optimal solution that could, theoretically, be computed if a complete Bayesian framework were known (and infinite computational power were available). The nature of the approximations, the deviations from optimality and the sensitivity to ad hoc parameters are specifically addressed