COR
2008
13 years 4 months ago
2008
Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such c...
NIPS
2003
13 years 6 months ago
2003
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making d...
KR
1989
Springer
13 years 8 months ago
1989
Springer
In this work we extend the work of Dean, Kaelbling, Kirman and Nicholson on planning under time constraints in stochastic domains to handle more complicated scheduling problems. I...
APN
1999
Springer
13 years 8 months ago
1999
Springer
Discrete event dynamic systems may have extremely large state spaces. For their analysis, it is usual to relax the description by removing the integrality constraints. Applying thi...
SPIN
2009
Springer
13 years 11 months ago
2009
Springer
Existing techniques for approximate storage of visited states in a model checker are too special-purpose and too DRAM-intensive. Bitstate hashing, based on Bloom filters, is good ...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
MDPs are an attractive formalization for planning, but realistic problems often have intractably large state spaces. When we only need a partial policy to get from a fixed start s...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object...