Planning for conjunctive goals
Artificial Intelligence
A critical look at Koblock's hierarchy mechanism
Proceedings of the first international conference on Artificial intelligence planning systems
Automatically generating abstractions for planning
Artificial Intelligence
Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Planning in a hierarchy of abstraction spaces
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Using Abstrips Abstractions -- Where do WeStand?
Artificial Intelligence Review
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
A weighted CSP approach to cost-optimal planning
AI Communications
| Hi-index | 0.00 |
We present a novel method for building ABSTRIPS-style abstraction hierarchies in planning. The aim of this method is to minimize the amount of backtracking between abstraction levels. Previous approaches have determined the criticality of operator preconditions by reasoning about plans directly. Here, we adopt a simpler and faster approach where we use numerical simulation of the planning process. We demonstrate the theoretical advantages of our approach by identifying some simple properties lacking in previous approaches but possessed by our method. We demonstrate the empirical advantages of our approach by a set of four benchmark experiments using the ABTWEAK system. We compare the quality of the abstraction hierarchies generated with those built by the ALPINE and HIGHPOINT algorithms.