A Markov Decision Process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagen...
This paper introduces a choice-based method that for the first time makes it possible to quantitatively measure regret theory, one of the most popular models of decision under unc...
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
We proposed a method to quantify the yield of an IT-investment portfolio in an environment of uncertainty and risk. For various common implementation scenarios such as growing dem...
This paper proposes a new approach for tackling the uncertainty and imprecision of the service evaluation process. Identifying suitable service offers, evaluating the offers and c...