@inproceedings{3864,
  abstract     = {Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification tinder the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures that are defined by mean-payoff automata, the optimal-synthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a long-run average reward objective, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. We present some experimental results showing optimal systems that were automatically generated in this way.},
  author       = {Chatterjee, Krishnendu and Henzinger, Thomas A and Jobstmann, Barbara and Singh, Rohit},
  location     = {Edinburgh, United Kingdom},
  pages        = {380 -- 395},
  publisher    = {Springer},
  title        = {{Measuring and synthesizing systems in probabilistic environments}},
  doi          = {10.1007/978-3-642-14295-6_34},
  volume       = {6174},
  year         = {2010},
}