Research map / Learning theory and algorithms / Combinatorial and search optimization
Making Translations to Classical Planning Competitive with Other HTN Planners
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add 2-Regularisation to HTN2SAS (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | coverage: 567 vs 594 | Helped |
| Add method compression to HTN2SAS (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | coverage: 567 vs 632 | Helped |
| Add both 2-Regularisation and method compression (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | coverage: 567 vs 712 | Helped |
| Add 2-Regularisation to HTN2SAS (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | IPC score: 8.3 vs 11.0 | Helped |
| Add method compression to HTN2SAS (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | IPC score: 8.3 vs 11.7 | Helped |
| Add both 2-Regularisation and method compression (ablation) | HTN2SAS on IPC 2020 TO-HTN · IPC 2020 TO-HTN benchmark set | coverage: 712 | Helped |
| Ground the HTN model before translating instead of translating the lifted model | HTN2SAS versus HTN2STRIPS · IPC 2020 TO-HTN benchmark set | IPC score: 8.3 vs 8.8 | Helped |
| Use the Push encoding instead of the Base encoding (ablation) | HTN2SAS on IPC 2020 PO-HTN · IPC 2020 PO-HTN benchmark set | coverage: 78 vs 105 | Helped |
| Use the parallel-sequences encoding where applicable instead of Push (ablation) | HTN2SAS on IPC 2020 PO-HTN · IPC 2020 PO-HTN benchmark set | IPC score: 3.0 to 3.4 | Helped |
| Replace the Push encoding with the parallel-sequences encoding (ablation) | HTN2SAS on IPC 2020 PO-HTN · IPC 2020 PO-HTN benchmark set | coverage: 105 vs 100 | Hurt |
| Use the parallel-sequences encoding only when the instance has that structure; otherwise use Push (ablation) | HTN2SAS on IPC 2020 PO-HTN · IPC 2020 PO-HTN benchmark set | coverage: 75 vs 105 | Helped |
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