Learning Features and Abstract Actions for Computing Generalized Plans
Authors: Blai Bonet, Guillem Francès, Hector Geffner2703-2710
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results We evaluate the computational model on four generalized problems Q. For each Q, we select a few training instances P in Q by hand, from which the sample sets S are drawn. S is constructed by collecting the first m states generated by a breadth-first search, along with the states generated in an optimal plan. The plans ensure that S contains some goal states and provide the state transitions that are marked as goal relevant when constructing the theory TG(S, F), which is the one used in the experiments. S is closed by fully expanding the states selected. The value of m is chosen so that the resulting number of transitions in S, which depends on the branching factor, is around 500. The bound k for F = Fk is set to 8. Distance features dist are used only in the last problem. The Weighted-Max Solver is Open-WBO (Martins, Manquinho, and Lynce 2014) and the FOND planner is SAT-FOND (Geffner and Geffner 2018). The translation from Q F to Q+ F is very fast, in the order of 0.01 seconds in all cases. The whole computational pipeline summarized by the steps 1 6 above is processed on Intel Xeon E5-2660 CPUs with time and memory cutoffs of 1h and 32GB. Table 1 summarizes the relevant data for the problems, including the size of the CNF encodings corresponding to the theories T and TG. |
| Researcher Affiliation | Academia | Blai Bonet Universidad Sim on Bol ıvar Caracas, Venezuela bonet@usb.ve Guillem Franc es University of Basel Basel, Switzerland guillem.frances@unibas.ch Hector Geffner ICREA & Universitat Pompeu Fabra Barcelona, Spain hector.geffner@upf.edu |
| Pseudocode | No | The paper describes computational steps but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper refers to a 'Translator available at https://github.com/bonetblai/qnp2fond' for a specific conversion step, but no explicit statement or link is provided for the open-source code of the full methodology described in the paper (e.g., the feature learning using Max-SAT). |
| Open Datasets | No | The paper refers to instances from domains like 'Blocksworld', 'Qgripper', and 'Qreward' and states 'we select a few training instances P in Q by hand', but does not provide specific access information (links, DOIs, repositories, or formal citations with authors and year) for these instances or the sample sets derived from them. |
| Dataset Splits | No | The paper mentions 'training instances' and 'sample sets S are drawn' but does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | The whole computational pipeline summarized by the steps 1 6 above is processed on Intel Xeon E5-2660 CPUs with time and memory cutoffs of 1h and 32GB. |
| Software Dependencies | Yes | The Weighted-Max Solver is Open-WBO (Martins, Manquinho, and Lynce 2014) and the FOND planner is SAT-FOND (Geffner and Geffner 2018). |
| Experiment Setup | Yes | The value of m is chosen so that the resulting number of transitions in S, which depends on the branching factor, is around 500. The bound k for F = Fk is set to 8. |