# Improving Continuous-time Conflict Based Search

arXiv: https://arxiv.org/abs/2101.09723 · HTML: https://mlsanity.com/paper/2101.09723/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)

## Built on by (most cited first, 11 of 11)

- [SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks](https://mlsanity.com/paper/2502.19913/) (2025)
- [Multi-agent Path Finding for Cooperative Autonomous Driving](https://mlsanity.com/paper/2402.00334/) (2024)
- [Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding](https://mlsanity.com/paper/2404.16379/) (2024)
- [Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search](https://mlsanity.com/paper/2312.16106/) (2023)
- [WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning](https://mlsanity.com/paper/2508.01495/) (2025)
- [Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks](https://mlsanity.com/paper/2309.10275/) (2023)
- [On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning](https://mlsanity.com/paper/2408.09028/) (2024)
- [Multi-agent Path Finding in Continuous Environment](https://mlsanity.com/paper/2409.10680/) (2024)
- [Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading](https://mlsanity.com/paper/2501.02770/) (2025)
- [Optimal Planning in a Dynamic World](https://mlsanity.com/paper/2610.03312/) (2026)
- [Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2610.09034/) (2026)

## Abstract (arXiv)

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and 2k-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains.