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AAAI
2006
13 years 7 months ago
Improving Approximate Value Iteration Using Memories and Predictive State Representations
Planning in partially-observable dynamical systems is a challenging problem, and recent developments in point-based techniques such as Perseus significantly improve performance as...
Michael R. James, Ton Wessling, Nikos A. Vlassis
UAI
2008
13 years 7 months ago
Sparse Stochastic Finite-State Controllers for POMDPs
Bounded policy iteration is an approach to solving infinitehorizon POMDPs that represents policies as stochastic finitestate controllers and iteratively improves a controller by a...
Eric A. Hansen
AIPS
2008
13 years 8 months ago
Efficient ADD Operations for Point-Based Algorithms
During the past few years, point-based POMDP solvers have gradually scaled up to handle medium sized domains through better selection of the set of points and efficient backup met...
Guy Shani, Pascal Poupart, Ronen I. Brafman, Solom...
AAAI
2004
13 years 7 months ago
Dynamic Programming for Partially Observable Stochastic Games
We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable M...
Eric A. Hansen, Daniel S. Bernstein, Shlomo Zilber...
JAIR
2010
115views more  JAIR 2010»
13 years 4 months ago
An Investigation into Mathematical Programming for Finite Horizon Decentralized POMDPs
Decentralized planning in uncertain environments is a complex task generally dealt with by using a decision-theoretic approach, mainly through the framework of Decentralized Parti...
Raghav Aras, Alain Dutech