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JAIR
2008
107views more  JAIR 2008»
15 years 1 months ago
Planning with Durative Actions in Stochastic Domains
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
Mausam, Daniel S. Weld
JCP
2008
139views more  JCP 2008»
15 years 1 months ago
Agent Learning in Relational Domains based on Logical MDPs with Negation
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
Song Zhiwei, Chen Xiaoping, Cong Shuang
JAIR
2006
160views more  JAIR 2006»
15 years 1 months ago
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
JMLR
2006
116views more  JMLR 2006»
15 years 1 months ago
Point-Based Value Iteration for Continuous POMDPs
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
CCE
2004
15 years 1 months ago
An algorithmic framework for improving heuristic solutions: Part II. A new version of the stochastic traveling salesman problem
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...
Jaein Choi, Jay H. Lee, Matthew J. Realff