Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Numerical methods for stochastic control problems in continuous time
Numerical methods for stochastic control problems in continuous time
Bounded-parameter Markov decision process
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Reachability Analysis for Uncertain SSPs
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Robust Control of Markov Decision Processes with Uncertain Transition Matrices
Operations Research
Bounded parameter Markov decision processes with average reward criterion
COLT'07 Proceedings of the 20th annual conference on Learning theory
Probabilistic model checking of complex biological pathways
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Probabilistic verification of uncertain systems using bounded-parameter markov decision processes
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Model-Checking markov chains in the presence of uncertainties
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Solving H-horizon, stationary Markov decision problems in time proportional to log(H)
Operations Research Letters
Fuzzy Markovian decision processes: Application to queueing systems
Computers & Mathematics with Applications
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Verification of reachability properties for probabilistic systems is usually based on variants of Markov processes. Current methods assume an exact model of the dynamic behavior and are not suitable for realistic systems that operate in the presence of uncertainty and variability. This research note extends existing methods for Bounded-parameter Markov Decision Processes (BMDPs) to solve the reachability problem. BMDPs are a generalization of MDPs that allows modeling uncertainty. Our results show that interval value iteration converges in the case of an undiscounted reward criterion that is required to formulate the problems of maximizing the probability of reaching a set of desirable states or minimizing the probability of reaching an unsafe set. Analysis of the computational complexity is also presented.