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Found 8 papers, 0 papers with code
Playing Games with your PET: Extending the Partial Exploration Tool to Stochastic Games
We present version 2. 0 of the Partial Exploration Tool (PET), a tool for verification of probabilistic systems.
What Are the Odds? Improving the foundations of Statistical Model Checking
Markov decision processes (MDPs) are a fundamental model for decision making under uncertainty.
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.
Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form.
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts
To this end, we also provide a graphical user interface.
Anytime Guarantees for Reachability in Uncountable Markov Decision Processes
As this problem is undecidable in general, assumptions on the MDP are necessary.
dtControl: Decision Tree Learning Algorithms for Controller Representation
In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.
PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games
Statistical model checking (SMC) is a technique for analysis of probabilistic systems that may be (partially) unknown.
