Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a relative conflict weight r=5 to weighted FOCAL | WF-EECBS · 8 MAPF maps, w_so=2 | median speedup: 1 -> 9.5 | Helped |
| Use weighted FOCAL with r=5 and w_h=8 | ECBS and EECBS · w_so=1.01 | median speedup: 0.45 | Hurt |
| Use weighted FOCAL with r=5 and w_h=8 | ECBS and EECBS · w_so=1.5 | median speedup: 3.5 and 3.7 | Helped |
| Add weighted cost-to-go to OPEN (w_h=1.5) | WO-EECBS · 8 MAPF maps, w_so=2 | median speedup: 1 -> 2.7 | Helped |
| Increase OPEN heuristic weight from w_h=1.5 to w_h=2 (ablation) | WO-EECBS · 8 MAPF maps, w_so=2 | median speedup: 2.7 -> 0.66 | Hurt |
| Use the improved lower bound (LB+) (ablation) | WO-EECBS (w_h=1.5) · 8 MAPF maps, w_so=2 | median speedup: 2.7 -> 2.4 | Hurt |
| Increase relative conflict weight from r=4 to r=8 (ablation) | WF-EECBS (w_h=4) · den312d, 150 agents, w_so=2 | speedup: 9.9 -> 6.1 | Hurt |
| Increase relative conflict weight from r=4 to r=16 (ablation) | WF-EECBS (w_h=4) · den312d, 150 agents, w_so=2 | speedup: 9.9 -> 3.9 | Hurt |
| Decrease relative conflict weight from r=4 to r=2 (ablation) | WF-EECBS (w_h=4) · den312d, 150 agents, w_so=2 | speedup: 9.9 -> 1.8 | Hurt |
| Add target conflicts to the low-level conflict heuristic (ablation) | EECBS · MAPF maps, updated conflict-heuristic analysis | runtime | Hurt |
| Remove target conflicts from conflict computation (ablation) | WF-EECBS versus EECBS · 8 MAPF maps | relative speedup: 2-100x -> 1.2-10x | Mixed |
Closest papers by meaning
- Windowed MAPF with Completeness Guarantees (AAAI 2025 · similarity 0.89)
- Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions (AAAI 2025 · similarity 0.87)
- Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic (AAAI 2025 · similarity 0.87)
- Learn to Follow: Decentralized Lifelong Multi-Agent Pathfinding via Planning and Learning (AAAI 2024 · similarity 0.86)
- Decentralized Monte Carlo Tree Search for Partially Observable Multi-Agent Pathfinding (AAAI 2024 · similarity 0.86)
- Speedup Techniques for Switchable Temporal Plan Graph Optimization (AAAI 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
