157 citations · 778 across the 15 of their papers we have counts for
7 papers · 1 filter
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
Ron Parr
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these appr…
Policy Iteration for Factored MDPs
Daphne Koller, Ron Parr
Many large MDPs can be represented compactly using a dynamic Bayesian network. Although the structure of the value function does not retain the structure of the process, recent wor…
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
Uri Lerner, Ron Parr
An important subclass of hybrid Bayesian networks are those that represent Conditional Linear Gaussian (CLG) distributions --- a distribution with a multivariate Gaussian component…
Value Function Approximation in Zero-Sum Markov Games
Michail Lagoudakis, Ron Parr
This paper investigates value function approximation in the context of zero-sum Markov games, which can be viewed as a generalization of the Markov decision process (MDP) framework…
Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (2007)
Ronald Parr, Linda S. van der Gaag
This is the Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, which was held in Vancouver, British Columbia, July 19 - 22 2007.
Efficient Solution Algorithms for Factored MDPs
C. Guestrin, D. Koller, R. Parr +1
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and th…