Inferring optimal utility values from a decision process
You might want to have a look at Inverse Reinforcement Learning. IRL tries to infer a reward function given observed optimal behavior in some MDP. I believe the framework was originally described in the 1998 paper Learning agents for uncertain environments by Stuart Russel. This paper and and those that cite it should be a good place to start, assuming IRL is a good fit for your problem.