Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Automatically generating abstractions for planning
Artificial Intelligence
Expressive equivalence of planning formalisms
Artificial Intelligence - Special volume on planning and scheduling
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Counterexample-guided abstraction refinement for symbolic model checking
Journal of the ACM (JACM)
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The fast downward planning system
Journal of Artificial Intelligence Research
A general theory of additive state space abstractions
Journal of Artificial Intelligence Research
Planning with abstraction hierarchies can be exponentially less efficient
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The computational complexity of avoiding spurious states in state space abstraction
Artificial Intelligence
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
There are two major uses of abstraction in planning and search: refinement (where abstract solutions are extended into concrete solutions) and heuristics (where abstract solutions are used to compute heuristics for the original search space). These two approaches are usually viewed as unrelated in the literature. It is reasonable to believe, though, that they are related, since they are both intrinsically based on the structure of abstract search spaces. We take the first steps towards formally investigating their relationships, employing our recently introduced framework for analysing and comparing abstraction methods. By adding some mechanisms for expressing metric properties, we can capture concepts like admissibility and consistency of heuristics. We present an extensive study of how such metric properties relate to the properties in the original framework, revealing a number of connections between the refinement and heuristic approaches. This also provides new insights into, for example, Valtorta's theorem and spurious states.