3 citations · 10 across the 13 of their papers we have counts for
4 papers · 2 filters
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…
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
Gaia Saveri, Laura Nenzi, Luca Bortolussi +1
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable…
Learning Explainable and Better Performing Representations of POMDP Strategies
Alexander Bork, Debraj Chakraborty, Kush Grover +2
Strategies for partially observable Markov decision processes (POMDP) typically require memory. One way to represent this memory is via automata. We present a method to learn an au…