Probabilistic Robust Multi-Agent Path Finding

Atzmon, Dor; Stern, Roni; Felner, Ariel; Sturtevant, Nathan R.; Koenig, Sven

doi:10.1609/icaps.v30i1.6642

Cited by 22 publications

(19 citation statements)

References 11 publications

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“…For solving G-MAPF, we introduced G-CBS, and showed experimentally that although G-MAPF is flexible, G-CBS performs relatively close to MC-CBS and IkR-CBS, which are hand tailored for one type of agent. Future work can suggest policies for selecting conflicts to resolve (Boyarski et al 2015) and can adjust G-CBS for the case of probabilistic environments (Wagner and Choset 2017;Atzmon et al 2020a).…”

Section: Discussionmentioning

confidence: 99%

Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Atzmon

Zax

Kivity

et al. 2021

SOCS

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Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous agents. We then show how two previous settings of large agents and k-robust agents are special cases of G-MAPF. Finally, we introduce G-CBS, a variant of the Conflict-Based Search (CBS) algorithm for G-MAPF, which does not cause significant extra overhead.

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Section: Discussionmentioning

confidence: 99%

Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Atzmon

Zax

Kivity

et al. 2021

SOCS

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“…There are several interesting directions for future work, namely to (1) study how to use mutexes to resolve semicardinal conflicts and non-cardinal conflicts; (2) apply mutex propagation to variants of MAPF problems where conflicts are implicitly represented, such as the robust MAPF problem (Atzmon et al 2018) and the MAPF problem with generalized conflicts (Hönig et al 2018); and (3) study mutex propagation for incomplete Boolean models of MAPF where the formula is built lazily (Surynek 2019).…”

Section: Discussionmentioning

confidence: 99%

Multi-Agent Path Finding with Mutex Propagation

Zhang

Surynek

et al. 2020

ICAPS

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Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in Conflict-Based Search (CBS), a popular optimal MAPF algorithm, and provides it with the ability to identify and reason with symmetries in MAPF. While existing work identifies a limited form of symmetries using rectangle reasoning and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH with rectangle reasoning, a state-of-the-art variant of CBS, with respect to runtime and success rate.

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“…Reasoning about computing paths and coordination that can be followed at dynamic unknown velocities has been substantially less studied. Several works have worked on "K-Robust" MAPF which compute paths that can be executed with k delays without collisions (Atzmon et al 2018;Chen et al 2021). These works do not require additional coordination during execution, but are limited to instances where each agent is delayed at most k times.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Coordination in Multi-Agent Path Finding with Dynamic Execution

Wagner

Veerapaneni

Likhachev

2022

AIIDE

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Multi-agent Path Finding (MAPF) is an important problem in large games with many dynamic agents that need to follow space-time trajectories without inter-agent collisions. Modern MAPF solvers plan assuming that agents directly follow the space-time trajectories at known constant speeds without delays or speedups, resulting in rigid plans which need to be replanned if there are changes during execution. Instead we would like agents to be able to follow their computed paths with dynamic velocities while requiring minimal coordination with others to prevent collisions and deadlocks. One way to address this problem is to first produce collision free space-time paths and then compute a coordination controller that prevents collisions and deadlock during dynamic execution. This two step process prevents fully minimizing coordination as the initially planned space-time paths do not reason about coordination and can be arbitrarily bad. We introduce a novel paradigm and show how planning in space-coordination level, rather than space-time, allows us to simultaneously plan paths and a coordination controller. Our method, Space-Level Conflict-Based Search (SL-CBS), builds on the Conflict-Based Search framework and allows us to reason explicitly about coordination, producing paths as well as a coordination controller with bounded suboptimal minimal coordination. We show experimentally that this results in a 20-50% reduction in coordination compared to the closest state of the art solver.

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Probabilistic Robust Multi-Agent Path Finding

Cited by 22 publications

References 11 publications

Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Multi-Agent Path Finding with Mutex Propagation

Minimizing Coordination in Multi-Agent Path Finding with Dynamic Execution

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