Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks (1906.08291v1)

Abstract: The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.

• The paper standardizes MAPF terminology and introduces benchmark frameworks to compare algorithm performance.
• It distinguishes classical MAPF from its variants, detailing constraints like vertex conflicts and makespan minimization.
• Numerical experiments using ICBS highlight unsolvable instances, emphasizing the need for robust, scalable solutions in real-world applications.

Analysis of "Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks"

The paper "Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks" provides a thorough examination of the Multi-Agent Pathfinding (MAPF) problem, a pivotal challenge in automated planning involving multiple agents. The authors, Roni Stern et al., offer a comprehensive overview, standardizing terminology and introducing benchmark frameworks to facilitate evaluation and comparison of MAPF algorithms.

Overview of MAPF

MAPF involves planning non-colliding paths for multiple agents within an environment with specific constraints, with applications spanning automated warehousing and autonomous vehicles. The problem assumes critical importance due to these practical applications. The paper distinguishes between classical MAPF, where each agent occupies a single vertex at any time, and numerous extensions that introduce various complexities.

Classical MAPF

In classical MAPF, the fundamental input includes a graph, source, and target vertices for a set of agents. Conflicts such as vertex, edge, following, cycle, and swapping are core considerations, impacting the solvability of an instance. Objectives such as minimizing makespan and sum of costs are central to evaluating MAPF solutions. The treatment of agent behavior at target vertices—remain at target or disappear—adds another dimension to the modeling.

Extensions and Variants

The complexity of MAPF is expanded by considering weighted graphs, introducing variability in action durations, and applying constraints like robustness and formations. The paper navigates through MAPF with different restrictions, such as kinematic constraints or considering agents with significant spatial footprints. These variants align MAPF closer to real-world applications, acknowledging practical actuarial and kinematic factors.

Benchmarking and Evaluation

The authors introduce a structured benchmarking framework, highlighting grid-based challenges to test contemporary algorithms. This is complemented by the utilization of Asprilo, a framework for simulating scenarios in automated warehouses. Their emphasis on evaluating algorithms over a variety of scenarios promotes transparency and standardization in MAPF research.

Numerical Results and Implications

The paper includes numerical results from running ICBS (Increasing Cost Tree Search with Branch-and-Bound) on proposed benchmarks, revealing complex problem sets where many instances cannot be readily solved within constraints. This signals the ongoing challenge and scope for optimization in MAPF algorithms.

Theoretical and Practical Implications

The unification of terminology and systematic provision of benchmarks represents a step towards consistency in MAPF research methodologies. By setting a standardized baseline, the paper encourages more rigorous and comparative studies, potentially yielding more robust solutions applicable in dynamic, real-world settings. Future developments in AI could leverage these frameworks to enhance the practical deployment of multi-agent systems in logistics and intelligent transportation.

In conclusion, the paper by Stern et al. fosters an understanding of MAPF by addressing its complexities, promoting a unified approach to its paper, and laying a foundation for advancing research in this significant area of computer science.