Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Improving Continuous-time Conflict Based Search
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add disjoint splitting, prioritized conflicts, and high-level heuristic to CCBS | CCBS · MAPF_R instances | instances solved: 3,792 -> 5,659 | Helped |
| Add disjoint splitting, prioritized conflicts, and heuristic | CCBS · dense roadmap, 20 agents | success rate: 0.1 -> 0.8 | Helped |
| Add disjoint splitting but omit prioritized conflicts and heuristic (ablation) | CCBS · super-dense roadmap | agents solved: 20 -> 25 agents | Helped |
| Add high-level heuristic H (ablation) | CCBS+DS+PC · sparse, dense, and super-dense roadmaps; 100 hardest instances | CT-node expansions reduction: 26.5%, 21.6%, and 17.8% | Helped |
| Add prioritized conflicts (ablation) | CCBS · super-dense roadmap | performance | No clear effect |
| Prioritize conflicts by cost impact rather than only cardinality | CCBS · MAPF_R | expanded CT nodes | Helped |
| Use generalized SIPP to consider each safe interval overlapping an action landmark | CCBS-DS · MAPF_R | completeness and optimality | Helped |
| Use all three enhancements together | CCBS · sparse and dense roadmaps | runtime: up to 2 orders of magnitude | Helped |
| Increase grid connectivity | CCBS enhancements · MovingAI grids | benefit of enhancements | Hurt |
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