Risk Sensitive Probabilistic Planning with ILAO* and Exponential Utility Function

Markov Decision Process (MDP) has been used very efficiently to solve sequential decision-making problems. However, there are problems in which dealing with the risks of the environment to obtain a reliable result is more important than minimizing the total expected cost. MDPs that deal with this type of problem are called risk-sensitive Markov decision processes (RSMDP). In this paper we propose an efficient heuristic search algorithm that allows to obtain a solution by evaluating only the relevant states to reach the goal states starting from an initial state.