collaborators

5 papers

cs.LO2025

Stopping Criteria for Value Iteration on Concurrent Stochastic Reachability and Safety Games

Marta Grobelna, Jan Křetínský, Maximilian Weininger

We consider two-player zero-sum concurrent stochastic games (CSGs) played on graphs with reachability and safety objectives. These include degenerate classes such as Markov decisio…

cs.AI2025

Explaining Control Policies through Predicate Decision Diagrams

Debraj Chakraborty, Clemens Dubslaff, Sudeep Kanav +2

Safety-critical controllers of complex systems are hard to construct manually. Automated approaches such as controller synthesis or learning provide a tempting alternative but usua…

cs.AI2024

Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees

Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav +1

Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability. Since in general optimal polici…

cs.LG2024

Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces

Vahid Hashemi, Jan Křetínský, Sabine Rieder +2

Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous…

cs.AI2024

1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav +4

Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when…