Multi-agent pathfinding is the task of navigating a set of mobile agents in a shared environment such that they avoid collisions. Finding an optimal solution in terms of the length of the plan is known to be a computationally hard problem (NP-Hard). In general, there are two schools of optimal algorithms: search-based and reduction-based. While search-based algorithms excel in solving large maps where few conflicts can be expected, reduction-based algorithms excel in smaller instances even when agents interact often. However, the reduction-based approaches lag behind in large instances, even with few agents. To mitigate this, a subgraph pruning method was introduced to prune unnecessary vertices to decrease the size of the instance. The pruning is based on the shortest paths for each agent. In the original study, the authors randomly selected the shortest routes. In this study, we replicate the overall approach while selecting the initial shortest path with more care. We provide several approaches for selecting one of the possible shortest paths and experimentally compare them. We note that when the makespan optimal plan is needed, not all agents are required to use the shortest path, as only the longest path dictates the makespan. Using this observation, we also introduce an approach that selects longer paths for some agents if it helps to reduce the total number of interactions between agents. We provide an experimental comparison of all proposed approaches and show that the latter performs significantly better, in most cases outperforming any approach that strictly selects only the shortest path.

Multi-agent pathfinding is the task of navigating a set of agents in a shared environment without collisions. Finding an optimal plan is a computationally hard problem, therefore, one may want to sacrifice optimality for faster computation time. In this paper, we present our preliminary work on finding a valid solution using only a predefined path for each agent with the possibility of adding wait actions. This restriction makes some instances unsolvable, however, we show instances where this approach is guaranteed to find a solution.

This paper extends a study on improving the performance of reduction-based solvers for the problem of multi-agent pathfinding. The task is to navigate a set of agents in a graph without collisions. Solvers that reduce this problem to other formalisms often have issues scaling to larger instances in terms of the graph size. A previous study suggests that pruning the graph of most vertices based on a randomly chosen shortest path for each agent. In this paper, we study the effect of different choices of these paths.

Multi-Agent Path Finding (MAPF) deals with the problem of finding collision-free paths for a set of agents moving in a shared environment, while each agent has specified its own destination. Colored MAPF generalizes MAPF by defining groups of agents that share a set of destination locations. In the paper, we evaluate several approaches to optimally solve colored MAPF problem, namely, a method based on network flows, an extended version of conflict-based search, and two models using Boolean satisfiability. We also investigate methods for obtaining lower bounds on optimal solutions based on constraint and continuous relaxation techniques.

Multi-Agent Path Finding (MAPF) deals with finding collision free paths for a set of agents (robots) moving on a graph. The interest in MAPF in the research community started to increase recently partly due to practical applications in areas such as warehousing and computer games. However, the academic community focuses mostly on solving the abstract version of the problem (moving of agents on the graph) with only a few results on real robots. The presented software MAPF Scenario provides a tool for specifying MAPF problems on grid maps, solving the problems using various abstractions (for example, assuming rotation actions or not), simulating execution of plans, and translating the abstract plans to control programs for small robots Ozobots. The tool is intended as a research platform for evaluating abstract MAPF plans on real robots and as an educational and demonstration tool bridging the areas of artificial intelligence and robotics.

Multi-Agent Path Finding (MAPF) deals with finding collision-free paths for a set of agents. It is a special case of a planning problem with only two actions - move and wait - and with one major constraint of no collision between the agents. The paper addresses the question of how to model MAPF as a classical sequential planning problem. Several models in the PDDL language are proposed and empirically compared.

Multi-agent pathfinding (MAPF) is the problem of moving a group of agents to a set of target destinations while avoiding collisions. In this work, we study the online version of MAPF where new agents appear over time. Several variants of online MAPF are defined and analyzed theoretically, showing that it is not possible to create an optimal online MAPF solver. Nevertheless, we propose effective online MAPF algorithms that balance solution quality, runtime, and the number of plan changes an agent makes during execution.

The problem of multi-agent path finding (MAPF) is studied in this paper. Solving MAPF optimally is a computationally hard problem and many different optimal algorithms have been designed over the years. These algorithms have good runtimes for some problem instances, while performing badly for other instances. Interestingly, these hard instances are often different across the algorithms. This leads to an idea of combining the strengths of different algorithms in such a way that an input problem instance is split into disjoint subproblems and each subproblem is solved by appropriate algorithm resulting in faster computation than using either of the algorithms for the whole instance. By manual problem decomposition we will empirically show that the above idea is viable. We will also sketch a possible future work on automated problem decomposition.

Multi-agent path finding (MAPF) is the problem to find collision-free paths for a set of agents (mobile robots) moving on a graph. There exists several abstract models describing the problem with various types of constraints. The demo presents software to evaluate the abstract models when the plans are executed on Ozobots, small mobile robots developed for teaching programming. The software allows users to design the grid-like maps, to specify initial and goal locations of robots, to generate plans using various abstract models implemented in the Picat programming language, to simulate and to visualise execution of these plans, and to translate the plans to command sequences for Ozobots.

Multi-agent path finding (MAPF) deals with the problem of finding collision-free paths for a set of agents. Each agent moves from its start location to its destination location in a shared environment represented by a graph. Reduction-based solving approaches for MAPF, for example reduction to SAT, exploit a time-expended layered graph, where each layer corresponds to specific time. Hence, these approaches are natural for minimizing makespan (the shortest time till all agents reach their destinations). Modeling the other frequently used objective, namely Sum of Costs (SOC; sum of paths lengths of all agents) is more difficult as the solution with the smallest SOC may not be reached in the time-expended graph with the smallest makespan. In this paper we suggest two novel approaches to estimate the makespan, that guarantees existence of a SOC-optimal solution. The approaches are empirically compared with an existing reduction-based method as well as with the state-of-the-art search-based optimal MAPF solver.

Multi-agent path finding is the problem of navigating multiple agents from their current locations to their goal locations in such a way that there are no collisions between the agents. The classical definition of the problem assumes that the set of agents is unchangeable, and that the distances in the graph are homogeneous. We propose to add to the problem specification a set of new attributes to bring it closer to the real world. These attributes include varying distances, number of agents that can occupy an edge or node, and dynamic appearance of new agents.