Deordering and Numeric Macro Actions for Plan Repair
Authors: Enrico Scala, Pietro Torasso
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the feasibility of the approach, the paper reports experiments in various domains from the International Planning Competition. Results show (i) the competitiveness of the strategy in terms of coverage, time and quality of the resulting plans wrt current approaches, and (ii) the actual independence from the planner employed. |
| Researcher Affiliation | Academia | Enrico Scala Research School of Computer Science Australian National University enrico.scala@anu.edu.au Pietro Torasso Dipartimento di Informatica Universita degli Studi di Torino torasso@di.unito.it |
| Pseudocode | Yes | Algorithm 1 summarizes the main steps involved. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is openly available. |
| Open Datasets | No | While the paper mentions using domains from the International Planning Competition (IPC), it describes generating custom variant problems for plan repair. It does not provide concrete access information (link, DOI, citation for specific generated problems) for a publicly available training dataset. |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test dataset splits with percentages or sample counts. It describes generating test cases but does not mention validation splits. |
| Hardware Specification | Yes | Experiments ran on Ubuntu 10.04 with an Intel Core Duo@2.53GHz cpu and 4 GB of Ram |
| Software Dependencies | No | The paper mentions using Colin and Metric-FF planners but does not specify their version numbers or other software dependencies with versions, which is required for reproducibility. |
| Experiment Setup | Yes | In our experiments we measured the average time taken by the preprocessing step over the total time taken for solving each single test case, and we have seen that this time takes up to the 77% in the Rover Domain (explaining the huge success of MADE over CLMA) while is just the 15% in the Zeno Travel Plus. |