Learning to Rank for Synthesizing Planning Heuristics
Authors: Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on recent International Planning Competition problems show that the Rank SVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression. |
| Researcher Affiliation | Academia | Caelan Reed Garrett, Leslie Pack Kaelbling, Tom as Lozano-P erez MIT CSAIL Cambridge, MA 02139 USA {caelan, lpk, tlp}@csail.mit.edu |
| Pseudocode | No | The information is insufficient. The paper describes its methods in narrative text and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The information is insufficient. The paper mentions using existing open-source frameworks and libraries (Fast Downward, dlib) for implementation but does not state that the authors' own code for the described methodology is publicly available or provide a link to it. |
| Open Datasets | Yes | We experimented on four domains from the 2014 IPC learning track [Vallati et al., 2015]: elevators, transport, parking, and no-mystery. |
| Dataset Splits | Yes | We select an appropriate value of λ by performing domainwise leave-one-out cross validation (LOOCV): For different possible values of λ, and in a domain with n training problem, we train on data from n 1 training problems and evaluate the resulting heuristic on the remaining problem according to the RMSE loss function, and average the scores from holding out each problem instance. |
| Hardware Specification | Yes | Each planner was run on a single 2.5 GHz processor for 30 minutes with 5 GB of memory. |
| Software Dependencies | No | The information is insufficient. The paper mentions using 'the Fast Downward framework [Helmert, 2006]' and 'the dlib C++ machine learning library [King, 2009]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We select an appropriate value of λ by performing domainwise leave-one-out cross validation (LOOCV): For different possible values of λ, and in a domain with n training problem, we train on data from n 1 training problems and evaluate the resulting heuristic on the remaining problem according to the RMSE loss function, and average the scores from holding out each problem instance. |