Open AccessProceedings Article
Hierarchical A *: searching abstraction hierarchies efficiently
Robert C. Holte,M. B. Perez,Robert Zimmer,Alan MacDonald +3 more
- pp 530-535
TLDR
A new abstraction-induced search technique, "Hierarchical A*", is introduced that gets around two difficulties: first, by drawing from a different class of abstractions, "homomorphism abstractions," and, secondly, by using novel caching techniques to avoid repeatedly expanding the same states in successive searches in the abstract space.Abstract:
ion, in search, problem solving, and planning, works by replacing one state space by another (the "abstract" space) that is easier to search. The results of the search in the abstract space are used to guide search in the original space. For instance, the length of the abstract solution can be used as a heuristic for A* in searching in the original space. However, there are two obstacles to making this work efficiently. The first is a theorem (Valtorta, 1984) stating that for a large class of abstractions, "embedding abstractions," every state expanded by blind search must also be expanded by A* when its heuristic is computed in this way. The second obstacle arises because in solving a problem A* needs repeatedly to do a full search of the abstract space while computing its heuristic. This paper introduces a new abstraction-induced search technique, "Hierarchical A*," that gets around both of these difficulties: first, by drawing from a different class of abstractions, "homomorphism abstractions," and, secondly, by using novel caching techniques to avoid repeatedly expanding the same states in successive searches in the abstract space. Hierarchical A* outperforms blind search on all the search spaces studied.read more
Citations
More filters
Proceedings Article
Cooperative pathfinding
TL;DR: The results show that the new algorithms, especially WHCA*, are robust and efficient solutions to the Cooperative Pathfinding problem, finding more successful routes and following better paths than Local Repair A*.
Near Optimal Hierarchical Path-Finding.
TL;DR: HPA* (Hierarchical Path-Finding A*), a hierarchical approach for reducing problem complexity in path-finding on grid-based maps, which abstracts a map into linked local clusters and works very well in domains with a dynamically changing environment.
Journal ArticleDOI
Enhanced partial expansion A
Meir Goldenberg,Ariel Felner,Roni Stern,Guni Sharon,Nathan R. Sturtevant,Robert C. Holte,Jonathan Schaeffer +6 more
TL;DR: A novel variant of A* called Enhanced Partial Expansion A* (EPEA*) is presented that advances the idea of PEA* to address the time aspect and shows significant improvements in run-time and memory performance for several standard benchmark applications.
Proceedings Article
Partial pathfinding using map abstraction and refinement
TL;DR: This paper introduces Partial-Refinement A* (PRA*), which can fully interleave planning and acting through path abstraction and refinement, and demonstrates the etfectiveness of PRA* in the domain of real-time strategy (RTS) games.
Proceedings Article
Efficient triangulation-based pathfinding
Douglas Demyen,Michael Buro +1 more
TL;DR: This paper presents a method for abstracting an environment represented using constrained Delaunay triangulations in a way that significantly reduces pathfinding search effort, as well as better representing the basic structure of the environment.
References
More filters
Journal ArticleDOI
Automatically generating abstractions for planning
TL;DR: A completely automated approach to generating abstractions for planning using a tractable, domain-independent algorithm whose only input is the definition of a problem to be solved and whose output is an abstraction hierarchy that is tailored to the particular problem.
Journal ArticleDOI
Speeding up problem solving by abstraction: a graph oriented approach
TL;DR: Experiments comparing these techniques on a variety of problems show that alternating opportunism (AltO) a variant of the new technique, is uniformly superior to all the others.
Journal ArticleDOI
Machine Discovery of Effective Admissible Heuristics
TL;DR: A more general class of transformations, called abstractions, that are guaranteed to generate only admissible heuristics are defined and an implemented program (Absolver II) is described that uses a means-ends analysis search control strategy to discover abstracted problems that result in effective admissibleHeuristics.
Book ChapterDOI
A problem similarity approach to devising heuristics: first results
TL;DR: Evidence of a role for exploiting certain similarities among problems to transfer a heuristic from one problem to another, from an "easier" problem to a "harder" one is presented.
Journal ArticleDOI
A result on the computational complexity of heuristic estimates for the A* algorithm
TL;DR: The performance of a new heuristic search algorithm that uses a formal representation that contains enough information to compute the heuristic evaluation function h(n), as defined in the context of A*, without requiring a human expert to provide it is analyzed.