TY - GEN
T1 - Conflict-Free Multi-Agent Meeting
AU - Atzmon, Dor
AU - Freiman, Shahar Idan
AU - Epshtein, Oscar
AU - Shichman, Oran
AU - Felner, Ariel
N1 - Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Recently, a Multi-Directional Heuristic Search algorithm, called MM*, was introduced. MM* is a state-of-the-art MAM optimal solver that searches from multiple directions (one for each agent) and is guided by a heuristic function. Practically, a solution to MAM may contain conflicting paths. A related problem that plans conflict-free paths to a given set of goal locations is the Multi-Agent Path Finding problem (MAPF). In this paper, we solve the Conflict-Free Multi-Agent Meeting problem (CF-MAM). In CF-MAM, we find a meeting location for multiple agents (as in MAM) as well as conflict-free paths (as in MAPF) to that location. We introduce two novel algorithms, which combine MAM and MAPF solvers, for optimally solving CF-MAM. We compare both algorithms experimentally, showing the pros and cons of each algorithm.
AB - Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Recently, a Multi-Directional Heuristic Search algorithm, called MM*, was introduced. MM* is a state-of-the-art MAM optimal solver that searches from multiple directions (one for each agent) and is guided by a heuristic function. Practically, a solution to MAM may contain conflicting paths. A related problem that plans conflict-free paths to a given set of goal locations is the Multi-Agent Path Finding problem (MAPF). In this paper, we solve the Conflict-Free Multi-Agent Meeting problem (CF-MAM). In CF-MAM, we find a meeting location for multiple agents (as in MAM) as well as conflict-free paths (as in MAPF) to that location. We introduce two novel algorithms, which combine MAM and MAPF solvers, for optimally solving CF-MAM. We compare both algorithms experimentally, showing the pros and cons of each algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85124616938&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85124616938
T3 - 14th International Symposium on Combinatorial Search, SoCS 2021
SP - 147
EP - 149
BT - 14th International Symposium on Combinatorial Search, SoCS 2021
A2 - Ma, Hang
A2 - Serina, Ivan
PB - Association for the Advancement of Artificial Intelligence
T2 - 14th International Symposium on Combinatorial Search, SoCS 2021
Y2 - 26 July 2021 through 30 July 2021
ER -