Research map / Learning theory and algorithms / Combinatorial and search optimization
Tiebreaking Strategies for A* Search: How to Explore the Final Frontier
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use lifo instead of fifo second-level tiebreaking after h | A* with LMcut · IPC 1104 instances | coverage: 558 -> 565 | Helped |
| drop h-based tiebreaking, use lifo only (ablation) | A* with LMcut · IPC 1104 instances | coverage: 565 -> 556 | No clear effect |
| use fifo only (no h, no lifo) (ablation) | A* with LMcut · IPC 1104 instances | coverage: 556 -> 442 | Hurt |
| replace [h, fifo] with depth-based random strategy [h, rd, ro] | A* with LMcut · Zerocost 620 instances | coverage: 256 -> 294.2 | Helped |
| replace [h, lifo] with [h, rd, ro] | A* with LMcut · IPC 1104 instances | coverage: 565 -> 572.8 | Helped |
| random depth instead of first depth (ablation) | [h, ?d, ro] · LMcut Zerocost | coverage: 257.4 -> 294.2 | Helped |
| random depth instead of last depth (ablation) | [h, ?d, ro] · LMcut Zerocost | coverage: 286.6 -> 294.2 | Helped |
| add depth buckets to random tiebreaking (ablation) | [h, ro] · LMcut Zerocost | coverage: 264.9 -> 294.2 | Helped |
| add h-based first-level tiebreaking to [rd, ro] (ablation) | [rd, ro] · LMcut Zerocost | coverage: 279.9 -> 294.2 | Helped |
| epsilon-cost transformation instead of [h, rd, ro] (ablation) | A* with LMcut · LMcut Zerocost | coverage: 294.2 -> 261 | Hurt |
| mangle action names in domain files (ablation) | [h, rd, ro] · Zerocost 620 instances | coverage: 287.2 -> 288.7 | No clear effect |
Closest papers by meaning
- On the Completeness of Best-First Search Variants That Use Random Exploration (AAAI 2016 · similarity 0.87)
- Best-First Width Search: Exploration and Exploitation in Classical Planning (AAAI 2017 · similarity 0.86)
- Envelope-Based Approaches to Real-Time Heuristic Search (AAAI 2020 · similarity 0.86)
- Anchor Search: A Unified Framework for Suboptimal Bidirectional Search (AAAI 2025 · similarity 0.86)
- Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search (AAAI 2025 · similarity 0.85)
- Thought of Search: Planning with Language Models Through The Lens of Efficiency (NeurIPS 2024 · similarity 0.84)
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