Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS

• URL: http://arxiv.org/abs/2205.11624v5
• Date: Sun, 24 Mar 2024 20:03:38 GMT
• Title: Effective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS
• Authors: Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev,
• Abstract summary: Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. We show that, contrary to prevailing CBS beliefs, a weighted cost-to-go can be used effectively alongside the conflict.
• Score: 21.394413249216395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. Typically low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. In this paper, we show that, contrary to prevailing CBS beliefs, a weighted cost-to-go heuristic can be used effectively alongside the conflict heuristic in two possible variants. In particular, one of these variants can obtain large speedups, 2-100x, across several scenarios and suboptimal CBS methods. Importantly, we discover that performance is related not to the weighted cost-to-go heuristic but rather to the relative conflict heuristic weight's ability to effectively balance low-level and high-level work. Additionally, to the best of our knowledge, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization. Update March 2024: We found that the relative speedup decreases to around 1.2-10x depending on how the conflict heuristic is computed (see appendix for more details).

Related papers

• Decision-focused Graph Neural Networks for Combinatorial Optimization [62.34623670845006]
  An emerging strategy to tackle optimization problems involves the adoption of graph neural networks (GNNs) as an alternative to traditional algorithms. Despite the growing popularity of GNNs and traditional algorithm solvers in the realm of CO, there is limited research on their integrated use and the correlation between them within an end-to-end framework. We introduce a decision-focused framework that utilizes GNNs to address CO problems with auxiliary support.
  arXiv  Detail & Related papers  (2024-06-05T22:52:27Z)
• Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection [80.63946798650653]
  Decision centers on whether to use a large LLM with better performance or a smaller one with reduced costs. We propose a simpler solution; we use only the uncertainty of the generations of the small LLM as the decision criterion. Our experiments reveal this simple solution optimally balances cost and performance, outperforming existing methods on 25 out of 27 experimental setups.
  arXiv  Detail & Related papers  (2024-05-03T14:38:59Z)
• Estimating Barycenters of Distributions with Neural Optimal Transport [93.28746685008093]
  We propose a new scalable approach for solving the Wasserstein barycenter problem. Our methodology is based on the recent Neural OT solver. We also establish theoretical error bounds for our proposed approach.
  arXiv  Detail & Related papers  (2024-02-06T09:17:07Z)
• Model-based Causal Bayesian Optimization [74.78486244786083]
  We introduce the first algorithm for Causal Bayesian Optimization with Multiplicative Weights (CBO-MW) We derive regret bounds for CBO-MW that naturally depend on graph-related quantities. Our experiments include a realistic demonstration of how CBO-MW can be used to learn users' demand patterns in a shared mobility system.
  arXiv  Detail & Related papers  (2023-07-31T13:02:36Z)
• Efficient Diffusion Training via Min-SNR Weighting Strategy [78.5801305960993]
  We treat the diffusion training as a multi-task learning problem and introduce a simple yet effective approach referred to as Min-SNR-$gamma$. Our results demonstrate a significant improvement in converging speed, 3.4$times$ faster than previous weighting strategies. It is also more effective, achieving a new record FID score of 2.06 on the ImageNet $256times256$ benchmark using smaller architectures than that employed in previous state-of-the-art.
  arXiv  Detail & Related papers  (2023-03-16T17:59:56Z)
• TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers [64.88759709443819]
  We suggest learning the instance-dependent proxies that are supposed to notably increase the efficiency of the search. The first proxy we suggest to learn is the correction factor, i.e. the ratio between the instance independent cost-to-go estimate and the perfect one. The second proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path.
  arXiv  Detail & Related papers  (2022-12-22T14:26:11Z)
• Cost Splitting for Multi-Objective Conflict-Based Search [27.757328432175157]
  We focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a state-of-the-art MO-MAPF algorithm. We show that the standard splitting strategy used by MO-CBS can lead to duplicate search nodes and hence can duplicate the search effort that MO-CBS needs to make. We propose two new splitting strategies for MO-CBS, namely cost splitting and disjoint cost splitting.
  arXiv  Detail & Related papers  (2022-11-23T11:44:20Z)
• Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS) [2.320417845168326]
  Conflict-Based Search (CBS) is a widely used algorithm for solving multi-agent pathfinding problems optimally. To trade-off optimality for running time different variants of bounded sub-optimal CBS were designed. We show that Focal Search on both levels of CBS can be beneficial in a wide range of setups.
  arXiv  Detail & Related papers  (2022-09-20T11:05:14Z)
• Improving Continuous-time Conflict Based Search [19.36475688888736]
  Conflict-Based Search (CBS) is a powerful framework for optimally solving multi-agent path finding (MAPF) problems. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. We show how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-levelagents, to the continuous time setting of CCBS.
  arXiv  Detail & Related papers  (2021-01-24T14:34:25Z)
• EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding [40.0986954496361]
  Explicit Estimation CBS (EECBS) is a bounded-suboptimal variant of CBS that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node. EECBS runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances.
  arXiv  Detail & Related papers  (2020-10-03T14:19:00Z)
• Multi Agent Path Finding with Awareness for Spatially Extended Agents [0.5156484100374058]
  Path finding problems involve identification of a plan for conflict free movement of agents over a common road network. In this paper, we consider spatially extended agents which have a size comparable to the length of the road on which they travel. An optimal multi agent path finding approach for spatially-extended agents was proposed in the eXtended Conflict Based Search (XCBS) algorithm.
  arXiv  Detail & Related papers  (2020-09-20T05:40:04Z)

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.