Pathfinding with Lazy Successor Generation
- URL: http://arxiv.org/abs/2408.15443v1
- Date: Tue, 27 Aug 2024 23:25:25 GMT
- Title: Pathfinding with Lazy Successor Generation
- Authors: Keisuke Okumura,
- Abstract summary: We study a pathfinding problem where only locations are given, and edges are implicitly defined.
Despite its simple structure, this problem becomes non-trivial with a massive number of locations.
We propose a novel LaCAS* algorithm, which does not generate successors all at once but gradually generates successors as the search progresses.
- Score: 12.02023514105999
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study a pathfinding problem where only locations (i.e., vertices) are given, and edges are implicitly defined by an oracle answering the connectivity of two locations. Despite its simple structure, this problem becomes non-trivial with a massive number of locations, due to posing a huge branching factor for search algorithms. Limiting the number of successors, such as with nearest neighbors, can reduce search efforts but compromises completeness. Instead, we propose a novel LaCAS* algorithm, which does not generate successors all at once but gradually generates successors as the search progresses. This scheme is implemented with k-nearest neighbors search on a k-d tree. LaCAS* is a complete and anytime algorithm that eventually converges to the optima. Extensive evaluations demonstrate the efficacy of LaCAS*, e.g., solving complex pathfinding instances quickly, where conventional methods falter.
Related papers
- Evolving A* to Efficiently Solve the k Shortest-Path Problem (Extended Version) [0.0]
This paper introduces a new search algorithm with the same time complexity, which results from a natural evolution of A* thus, it preserves all its interesting properties.
Experiments in various testbeds show a significant improvement in performance over the state of the art, often by one or two orders of magnitude.
arXiv Detail & Related papers (2024-08-15T15:54:25Z) - LiteSearch: Efficacious Tree Search for LLM [70.29796112457662]
This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget.
Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
arXiv Detail & Related papers (2024-06-29T05:14:04Z) - SLOPE: Search with Learned Optimal Pruning-based Expansion [2.0618817976970103]
We present the Search with Learned Optimal Pruning-based Expansion (SLOPE)
It learns the distance of a node from a possible optimal path, which in turn reduces the size of the open list.
This ensures that the search explores only the region close to optimal paths while lowering memory and computational costs.
arXiv Detail & Related papers (2024-06-07T13:42:15Z) - A Metaheuristic Algorithm for Large Maximum Weight Independent Set
Problems [58.348679046591265]
Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum.
Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges.
We develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search framework.
arXiv Detail & Related papers (2022-03-28T21:34:16Z) - Exploring Complicated Search Spaces with Interleaving-Free Sampling [127.07551427957362]
In this paper, we build the search algorithm upon a complicated search space with long-distance connections.
We present a simple yet effective algorithm named textbfIF-NAS, where we perform a periodic sampling strategy to construct different sub-networks.
In the proposed search space, IF-NAS outperform both random sampling and previous weight-sharing search algorithms by a significant margin.
arXiv Detail & Related papers (2021-12-05T06:42:48Z) - Revisiting the Complexity Analysis of Conflict-Based Search: New
Computational Techniques and Improved Bounds [5.158632635415881]
State-of-the-art approach to computing optimal solutions is Conflict-Based Search (CBS)
We revisit the complexity analysis of CBS to provide tighter bounds on the algorithm's run-time in the worst-case.
arXiv Detail & Related papers (2021-04-18T07:46:28Z) - BR-NS: an Archive-less Approach to Novelty Search [70.13948372218849]
We discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS)
BR-NS does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search.
We conduct experiments to gain insight into its feasibility and dynamics as well as potential advantages over archive-based NS in terms of time complexity.
arXiv Detail & Related papers (2021-04-08T17:31:34Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - Evolutionary Algorithm and Multifactorial Evolutionary Algorithm on
Clustered Shortest-Path Tree problem [2.578242050187029]
Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem.
To enhance the performance of the search process, two approaches are proposed.
arXiv Detail & Related papers (2020-10-19T08:37:18Z) - Learning to Accelerate Heuristic Searching for Large-Scale Maximum
Weighted b-Matching Problems in Online Advertising [51.97494906131859]
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource.
We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
arXiv Detail & Related papers (2020-05-09T02:48:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.