Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Searching with Consistent Prioritization for Multi-Agent Path Finding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Systematically search partial priority orderings, adding priorities to resolve collisions | CBSw/P vs CBS · 20×20 grids and game maps | success rate | Helped |
| Use best-first search over partial priority orderings | CBSw/P vs CBS · 20×20 grids and game maps | runtime | Helped |
| Systematically search partial priority orderings | CBSw/P vs CBS · 20×20 grids and game maps | flowtime | Mixed |
| Dynamically search partial priority orderings instead of fixing a total order | PBS vs FIX · 20×20 grids | flowtime suboptimality: never more than 4% worse than optimal; FIX finds solutions that are more than 5% worse than optimal | Helped |
| Dynamically search partial priority orderings instead of fixing a total order | PBS vs FIX · 20×20 grids | success rate | Helped |
| Use a fixed total priority ordering | FIX · 20×20 grids | success rate | Hurt |
| Prioritize agents with shorter individually optimal paths | SH · 20×20 grids | success rate | Hurt |
| Plan with PBS rather than a fixed total order | PBS vs FIX · brc202d and lak503d | success rate | Helped |
| Plan with PBS rather than a fixed total order | PBS vs FIX · brc202d and lak503d | flowtime suboptimality | Helped |
| Remove agents from the grid after they first reach their targets | PBS and FIX · brc202d(WF) and lak503d(WF) | low-level node expansions | Helped |
| Scale PBS to 600 agents on a well-formed map | PBS · brc202d(WF) | runtime: 35.18 | Helped |
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