Research map / Learning theory and algorithms / Combinatorial and search optimization
Linear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use IP model instead of its LP relaxation for the heuristic (ablation) | interval-relaxation heuristic · numeric planning benchmarks | coverage: 147 -> 170 (h^{c,tr} LP vs IP) | Helped |
| Use IP instead of LP on propositional-heavy IPC domains (ablation) | h^c · Rover, Satellite, Depots, Zeno Travel | coverage | Hurt |
| Add landmark, relevance, dominance and inverse-action constraints to LP (ablation) | LP heuristic · numeric planning benchmarks | coverage | Helped |
| Add state equation constraints (ablation) | h^c · numeric planning benchmarks | coverage | Helped |
| Add sequencing constraints (8)-(9) (ablation) | h^c_IP · numeric planning benchmarks | solving time: coverage 173 -> 170 | Hurt |
| Use IP-based interval relaxation heuristics instead of h^lma+ and h^r_max | A* with IP heuristics · numeric planning benchmarks | states expanded: 17620.2 -> 504.1 | Helped |
| Solve cost partitioning of h^lma+ as IP instead of LP | h^lma+ · numeric planning benchmarks | coverage: 153 -> 143 | No clear effect |
Closest papers by meaning
- PDBs Go Numeric: Pattern-Database Heuristics for Simple Numeric Planning (AAAI 2025 · similarity 0.91)
- Towards Practical Classical Planning Compilations of Numeric Planning (AAAI 2025 · similarity 0.89)
- Symbolic Numeric Planning with Patterns (AAAI 2024 · similarity 0.87)
- Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code (NeurIPS 2025 · similarity 0.85)
- Graph Learning for Numeric Planning (NeurIPS 2024 · similarity 0.85)
- Novelty vs. Potential Heuristics: A Comparison of Hardness Measures for Satisficing Planning (AAAI 2024 · similarity 0.84)
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