Name
Abstract | ||
|---|---|---|
While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scala- bility. On the other hand, non-deterministic con- ditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with non-uniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques. |
Year | Venue | DocType |
|---|---|---|
2005 | Uncertainty in Artificial Intelligence | Conference |
Volume | Citations | PageRank |
abs/1207.1350 | 3 | 0.38 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Bryce | 1 | 173 | 11.83 |
| Subbarao Kambhampati | 2 | 3453 | 450.74 |