From the 1 of 6 linked papers with an AI index.
6 papers
PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance
Ali Asadi, Krishnendu Chatterjee, Pavol Kebis
The paper studies PAC learning for reachability objectives in turn‑based stochastic games, proposing a decentralized approach where each player learns privately without sharing alg…
Strongly Polynomial Time Complexity of Policy Iteration for Robust MDPs
Ali Asadi, Krishnendu Chatterjee, Ehsan Goharshady +3
Markov decision processes (MDPs) are a fundamental model in sequential decision making. Robust MDPs (RMDPs) extend this framework by allowing uncertainty in transition probabilitie…
ε-Stationary Nash Equilibria in Multi-player Stochastic Graph Games
Ali Asadi, Léonard Brice, Krishnendu Chatterjee +1
A strategy profile in a multi-player game is a Nash equilibrium if no player can unilaterally deviate to achieve a strictly better payoff. A profile is an -Nash equilibrium if…
Revealing POMDPs: Qualitative and Quantitative Analysis for Parity Objectives
Ali Asadi, Krishnendu Chatterjee, David Lurie +1
Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, w…
Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes
Ali Asadi, Krishnendu Chatterjee, Jakob de Raaij
Deterministic Markov Decision Processes (DMDPs) are a mathematical framework for decision-making where the outcomes and future possible actions are deterministically determined by…
Risk-aware Markov Decision Processes Using Cumulative Prospect Theory
Thomas Brihaye, Krishnendu Chatterjee, Stefanie Mohr +1
Cumulative prospect theory (CPT) is the first theory for decision-making under uncertainty that combines full theoretical soundness and empirically realistic features [P.P. Wakker…