# Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search

arXiv: https://arxiv.org/abs/2312.16106 · HTML: https://mlsanity.com/paper/2312.16106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search](https://mlsanity.com/paper/2103.07116/) (2021)
- [Efficient Large-Scale Multi-Drone Delivery Using Transit Networks](https://mlsanity.com/paper/1909.11840/) (2019)
- [Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm](https://mlsanity.com/paper/1703.04159/) (2017)
- [Improving Continuous-time Conflict Based Search](https://mlsanity.com/paper/2101.09723/) (2021)
- [Conflict-based Search for Multi-Robot Motion Planning with Kinodynamic Constraints](https://mlsanity.com/paper/2207.00576/) (2022)
- [CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints](https://mlsanity.com/paper/2011.00441/) (2020)
- [Collision Detection for Agents in Multi-Agent Pathfinding](https://mlsanity.com/paper/1908.09707/) (2019)

## Built on by (most cited first, 1 of 1)

- [GRACE: A Unified 2D Multi-Robot Path Planning Simulator & Benchmark for Grid, Roadmap, And Continuous Environments](https://mlsanity.com/paper/2603.10858/) (2026)

## Abstract (arXiv)

While the study of unit-cost Multi-Agent Pathfinding (MAPF) problems has been popular, many real-world problems require continuous time and costs due to various movement models. In this context, this paper studies symmetry-breaking enhancements for Continuous-Time Conflict-Based Search (CCBS), a solver for continuous-time MAPF. Resolving conflict symmetries in MAPF can require an exponential amount of work. We adapt known enhancements from unit-cost domains for CCBS: bypassing, which resolves cost symmetries and biclique constraints which resolve spatial conflict symmetries. We formulate a novel combination of biclique constraints with disjoint splitting for spatial conflict symmetries. Finally, we show empirically that these enhancements yield a statistically significant performance improvement versus previous state of the art, solving problems for up to 10% or 20% more agents in the same amount of time on dense graphs.