A Simple Approach to Solving Cooperative Path-Finding as Propositional Satisfiability Works Well

Surynek, Pavel

doi:10.1007/978-3-319-13560-1_66

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…More information on SAT encoding for the makespan variant can be found, e.g. in [51,43,52]. The detailed transformation of a question of whether there are non-conflicting paths in TEGs will shown in following sections.…”

Section: Sat Encoding For Optimal Makespanmentioning

confidence: 99%

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

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

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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.

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Section: Sat Encoding For Optimal Makespanmentioning

confidence: 99%

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

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

Self Cite

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“…We will show NPcompleteness of MAT and provide an algorithm for solving it by reduction to a series of SAT problems. In doing so, we use similar reductions as have been used for solving MAPF (Surynek 2014;Surynek et al 2016;Barták et al 2017). We empirically evaluate the runtime of our implementation with regard to varying input characteristics, such as the number of containers or the size of the environment, and movement constraints, such as whether or not the containers block each other.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Agent Transportation Problem

Bachor,

Bergdoll,

Nebel

2023

AAAI

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We introduce the multi-agent transportation (MAT) problem, where agents have to transport containers from their starting positions to their designated goal positions. Movement takes place in a common environment where collisions between agents and between containers must be avoided. In contrast to other frameworks such as multi-agent pathfinding (MAPF) or multi-agent pickup and delivery (MAPD), the agents are allowed to separate from the containers at any time, which can reduce the makespan and also allows for plans in scenarios that are unsolvable otherwise. We present a complexity analysis establishing the problem's NP-completeness and show how the problem can be reduced to a sequence of SAT problems when optimizing for makespan. A MAT solver is empirically evaluated with regard to varying input characteristics and movement constraints and compared to a MAPD solver that utilizes conflict-based search (CBS).

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“…A common technique for solving MAPF optimally is to reduce it to another problem -most often SAT (Surynek 2016;Barták and Svancara 2019) or ASP (Erdem et al 2013;Gebser et al 2018). This type of solver suffers from instances where the underlying graph is large.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent Pathfinding on Large Maps Using Graph Pruning: This Way or That Way? (Extended Abstract)

Švancara

Obermeier

Husár

et al. 2022

SOCS

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This paper extends a study on improving the performance of reduction-based solvers for the problem of multi-agent pathfinding. The task is to navigate a set of agents in a graph without collisions. Solvers that reduce this problem to other formalisms often have issues scaling to larger instances in terms of the graph size. A previous study suggests that pruning the graph of most vertices based on a randomly chosen shortest path for each agent. In this paper, we study the effect of different choices of these paths.

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A Simple Approach to Solving Cooperative Path-Finding as Propositional Satisfiability Works Well

Cited by 6 publications

References 24 publications

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

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

The Multi-Agent Transportation Problem

Multi-agent Pathfinding on Large Maps Using Graph Pruning: This Way or That Way? (Extended Abstract)

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