Measuring and Synthesizing Systems in Probabilistic Environments

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 under 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...

• Type: Conference
• Year: 2010
• Where: CAV
• Authors: Krishnendu Chatterjee, Thomas A. Henzinger, Barbara Jobstmann, Rohit Singh
• Keywords: CAV 2010, Hardware, Omega-regular Specifications, Preference Order, Weighted Automaton