Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants

2017/07/02 by Surynek, Pavel, Felner, Ariel, Stern, Roni +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1707.00228

Abstract

In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. In this paper we focus on finding suboptimal solutions for MAPF for the sum-of-costs variant. Recently, a SAT-based approached was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we present SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant algorithms. Experimental results show that in many case the SAT-based solver significantly outperforms the search-based solvers.

Related

• A Summary of Adaptation of Techniques from Search-based Optimal Multi-Agent Path Finding Solvers to Compilation-based Approach
• Multi-Agent Path Finding with Continuous Time Using SAT Modulo Linear Real Arithmetic
• CBS-Budget (CBSB): A Complete and Bounded Suboptimal Search for Multi-Agent Path Finding
• No Panacea in Planning: Algorithm Selection for Suboptimal Multi-Agent Path Finding
• Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities