资源论文Understanding Subgoal Graphs by Augmenting Contraction Hierarchies Tansel Uras and Sven Koenig

Understanding Subgoal Graphs by Augmenting Contraction Hierarchies Tansel Uras and Sven Koenig

2019-11-05 | |  55 |   40 |   0
Abstract Contraction hierarchies and (N-level) subgoal graphs are two preprocessing-based path-planning algorithms that have so far only been compared experimentally through the grid-based path-planning competitions, where both algorithms had undominated runtime/memory trade-offs. Subgoal graphs can be considered as a framework that can be customized to different domains through the choice of a reachability relation R that identifies pairs of nodes on a graph between which it is easy to find shortest paths. Subgoal graphs can exploit R in various ways to speed-up query times and reduce memory requirements. In this paper, we break down the differences between N-level subgoal graphs and contraction hierarchies, and augment contraction hierarchies with ideas from subgoal graphs to exploit R. We propose three different modifications, analyze their runtime/memory trade-offs, and provide experimental results on grids using canonical-freespace-reachability as R, which show that both N-level subgoal graphs and contraction hierarchies are dominated in terms of the runtime/memory trade-off by some of our new variants.

上一篇:Meta-Level Control of Anytime Algorithms with Online Performance Prediction Justin Svegliato and Kyle Hollins Wray and Shlomo Zilberstein

下一篇:A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...