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Found 15 papers, 1 papers with code
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.
Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks
In this work, we provide a more flexible framework where a neuron can be replaced with a linear combination of other neurons, improving the reduction.
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.
PAC Statistical Model Checking of Mean Payoff in Discrete- and Continuous-Time MDP
We provide the first algorithm to compute mean payoff probably approximately correctly in unknown MDP; further, we extend it to unknown CTMDP.
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
We introduce a similarity function on formulae of signal temporal logic (STL).
LTL-Constrained Steady-State Policy Synthesis
On the one hand, Linear Temporal Logic (LTL) is a popular example of a formalism for qualitative specifications.
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.
DeepAbstract: Neural Network Abstraction for Accelerating Verification
While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks.
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.
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.
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.
Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints
Assuming the support of the unknown transition function and a lower bound on the minimal transition probability are known in advance, we show that in MDPs consisting of a single end component, two combinations of guarantees on the parity and mean-payoff objectives can be achieved depending on how much memory one is willing to use.
Probabilistic Bisimulation: Naturally on Distributions
In contrast to the usual understanding of probabilistic systems as stochastic processes, recently these systems have also been regarded as transformers of probabilities.
Logic in Computer Science
