Proceedings of the first international conference on Artificial intelligence planning systems
Automatically generating abstractions for planning
Artificial Intelligence
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Planning and Control in Artificial Intelligence: A Unifying Perspective
Applied Intelligence
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
AI Communications
Prottle: a probabilistic temporal planner
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A parametric hierarchical planner for experimenting abstraction techniques
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning under uncertainty with abstraction hierarchies
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.00 |
This paper is a report on research towards the development of an abstraction-based framework for decision-theoretic planning. We make use of two planning approaches in the context of probabilistic planning: planning by abstraction and planning graphs. To create abstraction hierarchies our planner uses an adapted version of a hierarchical planner under uncertainty, and to search for plans, we propose a probabilistic planning algorithm based on Pgraphplan. The article outlines the main framework characteristics, and presents results on some problems found in the literature. Our preliminary results suggest that our planner can reduce the size of the search space, when compared with Pgraphplan, hierarchical planning under uncertainty and topdown dynamic programming.