Automatic Generation of High-Level State Features for Generalized Planning
Authors: Damir Lotinac, Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that we generate features for diverse generalized planning problems and hence, compute generalized plans without providing a prior high-level representation of the states. We also bring a new landscape of challenging benchmarks to classical planning since our compilation naturally models classification tasks as classical planning problems. In all experiments, we run the classical planner Fast Downward [Helmert, 2006] with the LAMA-2011 setting [Richter and Westphal, 2010] on a Intel Core i5 3.10GHz x 4 with a 4GB memory bound and time limit of 3600s. Table 1 summarizes the obtained results. |
| Researcher Affiliation | Academia | Damir Lotinac and Javier Segovia-Aguas and Sergio Jim enez and Anders Jonsson Dept. Information and Communication Technologies, Universitat Pompeu Fabra Roc Boronat 138, 08018 Barcelona, Spain {damir.lotinac,javier.segovia,sergio.jimenez,anders.jonsson}@upf.edu |
| Pseudocode | Yes | Figure 1: Planning program for finding the minimum element in a list of integers of size n. Instructions on lines 0 and 3, represented with diamonds, are conditional goto instructions that, respectively, jump to line 2 when i j and to line 0 when i 6= n. The outgoing left branch of a diamond indicates that the condition holds and the right branch that it does not. Instructions on lines 1 and 2 are sequential instructions and are represented with boxes. Finally, end marks the program termination. |
| Open Source Code | No | The paper does not provide any concrete access (link or explicit statement) to open-source code for the described methodology. |
| Open Datasets | Yes | This model is particularly natural for classification tasks in which both the examples and the classifier are described using logic. Michalski s train [Michalski et al., 2013] is a good example of such tasks. |
| Dataset Splits | No | The paper mentions evaluating on benchmarks and provides results in Table 1, but it does not specify any training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | In all experiments, we run the classical planner Fast Downward [Helmert, 2006] with the LAMA-2011 setting [Richter and Westphal, 2010] on a Intel Core i5 3.10GHz x 4 with a 4GB memory bound and time limit of 3600s. |
| Software Dependencies | No | The paper mentions using 'Fast Downward [Helmert, 2006]' and 'LAMA-2011 setting [Richter and Westphal, 2010]'. While these identify the software, they refer to the publication years of the papers describing them rather than explicit version numbers for the software components themselves (e.g., Fast Downward vX.Y). |
| Experiment Setup | Yes | In all experiments, we run the classical planner Fast Downward [Helmert, 2006] with the LAMA-2011 setting [Richter and Westphal, 2010] on a Intel Core i5 3.10GHz x 4 with a 4GB memory bound and time limit of 3600s. |