Variants of Independence Detection in SAT-Based Optimal Multi-agent Path Finding

Surynek, Pavel; Švancara, Jiří; Felner, Ariel; Boyarski, Eli

doi:10.1007/978-3-319-93581-2_7

Cited by 1 publication

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we describe how to integrate independence detection for MAPF (Standley, 2010b;Standley & Korf, 2011) into MDD-SAT. We summarize here and extend the results originally published as conference papers (Surynek et al, 2017b(Surynek et al, , 2017c.…”

Section: Independence Detection In Sat-based Approachsupporting

confidence: 74%

See 1 more Smart Citation

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

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

Self Cite

View full text Add to dashboard Cite

In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective 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. Our main contribution is a new SAT-based MAPF solver called MDD-SAT, that is directly aimed to optimally solve the MAPF problem under the sum-of-costs objective function. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, MDD-SAT is able to generate a reasonable number of Boolean variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. In addition, we show that concepts applicable in search-based solvers like ICTS and ICBS are applicable in the SAT-based approach as well. Specifically, we integrate independence detection, a generic technique for decomposing an MAPF instance into independent subproblems, into our SAT-based approach, and we design a relaxation of our optimal SAT-based solver that results in a bounded suboptimal SAT-based solver. Experimental evaluation on several domains shows that there are many scenarios where our SAT-based methods outperform state-of-the-art sum-of-costs search-based solvers, such as variants of the ICTS and ICBS algorithms.

show abstract

Section: Independence Detection In Sat-based Approachsupporting

confidence: 74%

“…Results presented in this work partly appeared in several conference papers (Surynek, Felner, Stern, & Boyarski, 2016a, 2016b, 2017aSurynek, Svancara, Felner, & Boyarski, 2017c, 2017b.…”

Section: Current Shortcomings and Contributionmentioning

confidence: 75%