Papers Multi-Agent Path Finding
“Multi-Agent Path Finding” 태그가 달린 논문 140편 · 필터 해제
Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Finding
Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT)…
Multi-Agent Path FindingNavigateAccelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various he…
Multi-Agent Path FindingOpen-Ended Question AnsweringAn Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information
Multi-agent path finding (MAPF) is the problem of moving agents to the goal vertex without collision. In the online MAPF problem, new agents may be added to the environment at any time, and the current agents have no inf…
Computational EfficiencyMulti-Agent Path FindingHeuristically Guided Compilation for Multi-Agent Path Finding
Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem …
Multi-Agent Path FindingMulti Agent Path Finding using Evolutionary Game Theory
In this paper, we consider the problem of path finding for a set of homogeneous and autonomous agents navigating a previously unknown stochastic environment. In our problem setting, each agent attempts to maximize a give…
Multi-Agent Path FindingCost Splitting for Multi-Objective Conflict-Based Search
The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such c…
Multi-Agent Path FindingMulti-Agent Path Finding via Tree LSTM
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, …
Multi-Agent Path Findingreinforcement-learningReinforcement Learning (RL)SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation
We propose an extension to the MAPF formulation, called SocialMAPF, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and corridor intersections. SocialMAPF is abl…
Motion PlanningMulti-Agent Path FindingSocial NavigationMulti-Goal Multi-Agent Pickup and Delivery
In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visi…
Multi-Agent Path FindingOptimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task …
Multi-Agent Path FindingPlan Execution for Multi-Agent Path Finding with Indoor Quadcopters
We study the planning and acting phase for the problem of multi-agent path finding (MAPF) in this paper. MAPF is a problem of navigating agents from their start positions to specified individual goal positions so that ag…
Multi-Agent Path FindingToward multi-target self-organizing pursuit in a partially observable Markov game
The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multi…
Decision MakingDeep Reinforcement LearningMulti-Agent Path FindingMulti-agent Reinforcement LearningGraph-Based Multi-Robot Path Finding and Planning
Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called Multi-Agent Path Finding (MAPF). This …
Multi-Agent Path FindingSchedulingEffective Integration of Weighted Cost-to-go and Conflict Heuristic within Suboptimal CBS
Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solver…
Multi-Agent Path FindingMulti-Agent Terraforming: Efficient Multi-Agent Path Finding via Environment Manipulation
Multi-agent pathfinding (MAPF) is concerned with planning collision-free paths for a team of agents from their start to goal locations in an environment cluttered with obstacles. Typical approaches for MAPF consider the …
Multi-Agent Path FindingThe Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications
We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, …
Multi-Agent Path FindingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these…
Motion PlanningMulti-Agent Path FindingTask and Motion PlanningConflict-Based Search for Explainable Multi-Agent Path Finding
In the Multi-Agent Path Finding (MAPF) problem, the goal is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location. In safety-critical applications, a hu…
Multi-Agent Path FindingLeveraging Experience in Lifelong Multi-Agent Pathfinding
In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF …
Multi-Agent Path FindingMulti-Agent Path Finding with Prioritized Communication Learning
Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has been introduced to alleviate real-time prob…
Multi-Agent Path FindingMulti-agent Reinforcement LearningPICO