Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use flexible flex distribution instead of requiring each agent's path to be bounded-suboptimal | EECBS · Large MAPF maps, w=1.01 | runtime | Helped |
| Use flexible flex distribution | EECBS · 7,000 MAPF instances on 8 maps; w=1.01 | sum of LB improvements: 66.7 -> 43.0 | Hurt |
| Use flexible flex distribution | EECBS · 7,000 MAPF instances on 8 maps; w=1.02 | sum of LB improvements: 54.2 -> 27.0 | Hurt |
| Use flexible flex distribution | EECBS · 7,000 MAPF instances on 8 maps; w=1.05 | sum of LB improvements: 30.4 -> 10.9 | Hurt |
| Use flexible flex distribution | EECBS · 7,000 MAPF instances on 8 maps; w=1.10 | sum of LB improvements: 13.0 -> 8.96 | Hurt |
| Add bypassing conflicts (ablation) | FEECBS · 7,000 MAPF instances on 8 maps | instances solved: +50 instances | Helped |
| Add flex restrictions and restart the high-level search with EECBS (ablation) | FEECBS · Small MAPF maps | success rate | Helped |
| Add low-level focal-A* search (ablation) | EECBS · MAPF benchmark maps | runtime | Helped |
| Add flex restrictions, restart with EECBS, and low-level focal-A* search (ablation) | FEECBS · Warehouse map; w=1.01 | success rate | Helped |
| Use flexible flex distribution | EECBS · room map, 40 agents; w=1.05 | CT nodes selected by Rule (E3): 16 -> 52 | Hurt |
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