Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search

Li, Jiaoyang; Felner, Ariel; Boyarski, Eli; Ma, Hang; Koenig, Sven

doi:10.24963/ijcai.2019/63

Cited by 97 publications

(99 citation statements)

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“…For instance, for CBS, a popular and efficient optimization considers cardinal conflicts [Boyarski et al, 2015], which are conflicts that can only be solved by increasing the lengths of the paths of all involved agents. This concept led to the integration of powerful heuristics in CBS Li et al, 2019a]. The adaptation of similar concepts for connectivity is however nontrivial.…”

Section: Discussionmentioning

confidence: 99%

Connect Multi-Agent Path Finding: Generation and Visualization

Queffelec

Sankur

Schwarzentruber

2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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Section: Discussionmentioning

confidence: 99%

Connect Multi-Agent Path Finding: Generation and Visualization

Queffelec

Sankur

Schwarzentruber

2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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“…In the high-level search, Felner suggests adding heuristics into CBS so that the conflicts are not arbitrarily chosen [48]. After that, Li found the improved heuristics to guide the high-level search [49]. Also, she introduced the CBS with disjoint splitting [50].…”

Section: Mapf For Mobile Roboticsmentioning

confidence: 99%

“…After that, Li found the improved heuristics to guide the high-level search [49]. Also, she introduced the CBS with disjoint splitting [50]. The main contribution of CBS with disjoint splitting is the novel terminology of positive constraints forcing the a i to be at v at timestep t. In this fashion, CBS with disjoint slitting reduces the amount of unnecessarily expanding the CT.…”

Section: Mapf For Mobile Roboticsmentioning

confidence: 99%

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

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Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

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“…First, search-based solvers (e.g., [7]), which use heuristic search as the main component. A state-of-the-art search-based approach is Conflict-Based Search (CBS) [8]- [10], which uses A* at its core. A second class is compilation-based solvers; for example, compilers from MAPF to Satisfiability Testing (SAT) (e.g., [11]- [13]), and Answer-Set Programming (ASP) [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

2021

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Multi-agent pathfinding (MAPF) is the problem of finding k non-colliding paths connecting k given initial positions with k given goal positions on a given map. In its sum-of-costs variant, the total number of moves and wait actions performed by agents before they definitely reach the goal is minimized. Not surprisingly, since MAPF is combinatorial, a number of compilations to Boolean Satisfiability (SAT) and Answer Set Programming (ASP) exist. In this paper, we describe in detail the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Compared to existing ASP compilations, a distinguishing feature of our compilation is that the number of total clauses (after grounding) grow linearly with the number of agents, while existing compilations grow quadratically. In addition, the optimization objective is such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search-based sum-of-costs MAPF solvers when grids are congested with agents. We also show that our approach is competitive with a SAT-based approach when follow conflicts are taken into account. We also explore the potential of our solver when finding makespanoptimal solutions, in which makespan is minimized first and then cost is minimized. Our results show that makespan-optimal solutions are slightly suboptimal in most benchmarks. Moreover, our MAPF solver, when run in that mode, is faster and scales better.

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Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search

Cited by 97 publications

References 0 publications

Connect Multi-Agent Path Finding: Generation and Visualization

Connect Multi-Agent Path Finding: Generation and Visualization

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

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