activity
20242026
most citedRandomise Alone, Reach as a Team

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Quantitative Analysis of -Regular Robust MDPs

Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady +3

Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consi…

cs.CC2026

On the Complexity of Discounted Robust MDPs with Uncertainty Sets

Ali Asadi, Krishnendu Chatterjee, Alipasha Montaseri +1

A basic model in sequential decision making is the Markov decision process (MDP), which is extended to Robust MDPs (RMDPs) by allowing uncertainty in transition probabilities and o…

cs.GT2026★ 1 cited

Randomise Alone, Reach as a Team

Léonard Brice, Thomas A. Henzinger, Alipasha Montaseri +2

We study concurrent graph games where n players cooperate against an opponent to reach a set of target states. Unlike traditional settings, we study distributed randomisation: team…

cs.AI2025

Qualitative Analysis of -Regular Objectives on Robust MDPs

Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady +2

Robust Markov Decision Processes (RMDPs) generalize classical MDPs that consider uncertainties in transition probabilities by defining a set of possible transition functions. An ob…

cs.CC2024

Limit-sure reachability for small memory policies in POMDPs is NP-complete

Ali Asadi, Krishnendu Chatterjee, Raimundo Saona +1

A standard model that arises in several applications in sequential decision making is partially observable Markov decision processes (POMDPs) where a decision-making agent interact…