Partially Observable Markov Decision Processes incorporating epistemic uncertainties

The use of Markov Decision Processes for Inspection Maintenance and Rehabilitation of civil engineering structures relies on the use of several transition matrices related to the stochastic degradation process, maintenance actions and imperfect inspections. Point estimators for these matrices are usually used and they are evaluated using statistical inference methods and/or expert evaluation methods. Thus, considerable epistemic uncertainty often veils the true values of these matrices. Our contribution through this paper is threefold. First, we present a methodology for incorporating epistemic uncertainties in dynamic programming algorithms used to solve finite horizon Markov Decision Processes (which may be partially observable). Second, we propose a methodology based on the use of Dirichlet distributions which answers, in our sense, much of the controversy found in the literature about estimating Markov transition matrices. Third, we show how the complexity resulting from the use of Monte-Carlo simulations for the transition matrices can be greatly overcome in the framework of dynamic programming. The proposed model is applied to concrete bridge under degradation, in order to provide the optimal strategy for inspection and maintenance. The influence of epistemic uncertainties on the optimal solution is underlined through sensitivity analysis regarding the input data.