Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

02/17/2017
by   Hang Ma, et al.
0

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to developing faster methods for the standard formulation of the MAPF problem.

READ FULL TEXT

page 2

page 3

research
10/10/2017

AI Buzzwords Explained: Multi-Agent Path Finding (MAPF)

Explanation of the hot topic "multi-agent path finding"....
research
10/07/2022

Multi-Agent Systems for Computational Economics and Finance

In this article we survey the main research topics of our group at the U...
research
05/21/2019

Position Paper: From Multi-Agent Pathfinding to Pipe Routing

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision...
research
06/08/2017

Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in ar...
research
11/25/2018

An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview

Motivated by Shannon's model and recent rehabilitation of self-supervise...
research
11/24/2022

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop ...
research
07/24/2023

An algorithm with improved complexity for pebble motion/multi-agent path finding on trees

The pebble motion on trees (PMT) problem consists in finding a feasible ...

Please sign up or login with your details

Forgot password? Click here to reset