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» Acting Optimally in Partially Observable Stochastic Domains
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ICML
1999
IEEE
14 years 11 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
AAAI
2004
14 years 7 days 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...
FLAIRS
2009
13 years 8 months ago
Dynamic Programming Approximations for Partially Observable Stochastic Games
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
Akshat Kumar, Shlomo Zilberstein
ATAL
2004
Springer
14 years 4 months ago
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply si...
Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jef...
WIOPT
2011
IEEE
13 years 2 months ago
Network utility maximization over partially observable Markovian channels
Abstract—This paper considers maximizing throughput utility in a multi-user network with partially observable Markov ON/OFF channels. Instantaneous channel states are never known...
Chih-Ping Li, Michael J. Neely