3 papers
cs.AI2026
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.AI2025
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…
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…