A* is a classic and popular method for graphs search and path finding. It assumes the existence of a heuristic function h(u,t) that estimates the shortest distance from any input node u to the destination t. Traditionally, heuristics have been handcrafted by domain experts. However, over the last few years, there has been a growing interest in learning heuristic functions. Such learned heuristics estimate the distance between given nodes based on "features" of those nodes. In this paper we formalize and initiate the study of such feature-based heuristics. In particular, we consider heuristics induced by norm embeddings and distance labeling schemes, and provide lower bounds for the tradeoffs between the number of dimensions or bits used to represent each graph node, and the running time of the A* algorithm. We also show that, under natural assumptions, our lower bounds are almost optimal.
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A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model
- Award ID(s):
- 1634641
- PAR ID:
- 10065956
- Date Published:
- Journal Name:
- International Journal of Geographical Information Science
- Volume:
- 32
- Issue:
- 4
- ISSN:
- 1365-8816
- Page Range / eLocation ID:
- 637 to 662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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