Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00357052" target="_blank" >RIV/68407700:21240/22:00357052 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1613/jair.1.13318" target="_blank" >https://doi.org/10.1613/jair.1.13318</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1613/jair.1.13318" target="_blank" >10.1613/jair.1.13318</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Popis výsledku v původním jazyce

In the multi-agent path finding problem (MAPF) we are given a set of agents each with re- spective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach.

Název v anglickém jazyce

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Popis výsledku anglicky

In the multi-agent path finding problem (MAPF) we are given a set of agents each with re- spective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-17966S" target="_blank" >GA19-17966S: intALG-MAPFg: Inteligentní algoritmy pro zobecněné varianty multi-agetního hledání cest</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Artificial Intelligence Research

ISSN

1076-9757

e-ISSN

1943-5037

Svazek periodika

2022

Číslo periodika v rámci svazku

73

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

66

Strana od-do

553-618

Kód UT WoS článku

000755563700002

EID výsledku v databázi Scopus

2-s2.0-85125937283