8 citations · 17 across the 13 of their papers we have counts for
5 papers · 1 filter
Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties
Jessica Lally, Milad Kazemi, Nicola Paoletti
Counterfactual reasoning (asking what would have happened, had a decision been different) is a powerful tool for identifying causes of failure and explaining the behaviour of AI sy…
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives
Milad Kazemi, Mateo Perez, Fabio Somenzi +3
Recent advances in reinforcement learning (RL) have renewed interest in reward design for shaping agent behavior, but manually crafting reward functions is tedious and error-prone.…
Robust Counterfactual Inference in Markov Decision Processes
Jessica Lally, Milad Kazemi, Nicola Paoletti
This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make c…
Counterfactual Influence in Markov Decision Processes
Milad Kazemi, Jessica Lally, Ekaterina Tishchenko +2
Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs). Given an MDP path , this kind of inference allows us to…
Causal Temporal Reasoning for Markov Decision Processes
Milad Kazemi, Nicola Paoletti
We introduce , a new probabilistic temporal logic for the verification of Markov Decision Processes (MDP). PCFTL is th…