activity
20112013
most citedOptimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

354 citations · 626 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI201366 cited

Approximate Planning for Factored POMDPs using Belief State Simplification

David A. McAllester, Satinder Singh

We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that…

cs.AI201375 cited

On the Complexity of Policy Iteration

Yishay Mansour, Satinder Singh

Decision-making problems in uncertain or stochastic domains are often formulated as Markov decision processes (MDPs). Policy iteration (PI) is a popular algorithm for searching ove…

cs.GT20132 cited

Nash Convergence of Gradient Dynamics in Iterated General-Sum Games

Satinder Singh, Michael Kearns, Yishay Mansour

Multi-agent games are becoming an increasing prevalent formalism for the study of electronic commerce and auctions. The speed at which transactions can take place and the growing c…

cs.GT201336 cited

Fast Planning in Stochastic Games

Michael Kearns, Yishay Mansour, Satinder Singh

Stochastic games generalize Markov decision processes (MDPs) to a multiagent setting by allowing the state transitions to depend jointly on all player actions, and having rewards d…

cs.AI20123 cited

Predictive State Representations: A New Theory for Modeling Dynamical Systems

Satinder Singh, Michael James, Matthew Rudary

Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs…

cs.AI201220 cited

Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Matthew Rudary, Satinder Singh, David Wingate

Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hi…