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Found 9 papers, 1 papers with code
What Are the Odds? Improving the foundations of Statistical Model Checking
Markov decision processes (MDPs) are a fundamental model for decision making under uncertainty.
Learning Algorithms for Verification of Markov Decision Processes
The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios.
Reachability Poorman Discrete-Bidding Games
We consider, for the first time, {\em poorman discrete-bidding} in which the granularity of the bids is restricted and the higher bid is paid to the bank.
Guessing Winning Policies in LTL Synthesis by Semantic Learning
Firstly, checking whether a guessed strategy is winning is easier than constructing one.
Stopping Criteria for Value Iteration on Stochastic Games with Quantitative Objectives
In this paper, we provide the first stopping criteria for VI on SG with total reward and mean payoff, yielding the first anytime algorithms in these settings.
Correct Approximation of Stationary Distributions
A classical problem for Markov chains is determining their stationary (or steady-state) distribution.
Risk-aware Stochastic Shortest Path
We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP).
Anytime Guarantees for Reachability in Uncountable Markov Decision Processes
As this problem is undecidable in general, assumptions on the MDP are necessary.
Of Cores: A Partial-Exploration Framework for Markov Decision Processes
We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties.
