3 citations · 5 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Monitizer: Automating Design and Evaluation of Neural Network Monitors
Muqsit Azeem, Marta Grobelna, Sudeep Kanav +3
The behavior of neural networks (NNs) on previously unseen types of data (out-of-distribution or OOD) is typically unpredictable. This can be dangerous if the network's output is u…