Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Convex Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Succinct approximate convex pareto curves
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Quantitative multi-objective verification for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Two Views on Multiple Mean-Payoff Objectives in Markov Decision Processes
LICS '11 Proceedings of the 2011 IEEE 26th Annual Symposium on Logic in Computer Science
Markov decision processes with multiple objectives
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Assume-Guarantee verification for probabilistic systems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Approximating the pareto front of multi-criteria optimization problems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Towards communication-based steering of complex distributed systems
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
From software verification to `everyware' verification
Computer Science - Research and Development
Compositional probabilistic verification through multi-objective model checking
Information and Computation
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
Trading Performance for Stability in Markov Decision Processes
LICS '13 Proceedings of the 2013 28th Annual ACM/IEEE Symposium on Logic in Computer Science
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Multi-objective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linear programming, which limits the scale of systems that can be analysed and makes verification of time-bounded properties very difficult. We present a novel approach that addresses both of these shortcomings, based on the generation of successive approximations of the Pareto curve for a multi-objective model checking problem. We illustrate dramatic improvements in efficiency on a large set of benchmarks and show how the ability to visualise Pareto curves significantly enhances the quality of results obtained from current probabilistic verification tools.