競合ベース探索の完全性について：時間的相対的重複枝刈り
On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning
マルチエージェント経路探索のCBSが解なし問題で停止しない欠陥を、重複状態の検出・除去により解決するTRDPを提案。
著者: Thayne T Walker, Nathan R Sturtevant
分類: cs.AI, cs.RO
原文アブストラクト
Conflict-Based Search (CBS) algorithm for the multi-agent pathfinding (MAPF) problem is that it is incomplete for problems which have no solution; if no mitigating procedure is run in parallel, CBS will run forever when given an unsolvable problem instance. In this work, we introduce Temporally-Relative Duplicate Pruning (TRDP), a technique for duplicate detection and removal in both classic and continuous-time MAPF domains. TRDP is a simple procedure which closes the long-standing theoretic loophole of incompleteness for CBS by detecting and avoiding the expansion of duplicate states. TRDP is shown both theoretically and empirically to ensure termination without a significant impact on runtime in the majority of problem instances. In certain cases, TRDP is shown to increase performance significantly