Refining Abstraction Heuristics during Real-Time Planning
Authors: Rebecca Eifler, Maximilian Fickert, Jörg Hoffmann, Wheeler Ruml7578-7585
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test this idea empirically using Cartesian abstractions in the Fast Downward planner. Results on various benchmarks, including the sliding tile puzzle and several IPC domains, indicate that the approach can improve performance compared to traditional heuristic updating. |
| Researcher Affiliation | Academia | Rebecca Eifler, Maximilian Fickert, J org Hoffmann Saarland University, Saarland Informatics Campus Saarbr ucken, Germany {eifler,fickert,hoffmann}@cs.uni-saarland.de Wheeler Ruml University of New Hampshire Durham, NH USA ruml at cs.unh.edu |
| Pseudocode | No | The paper describes algorithms and procedures but does not include any formally labeled 'Pseudocode' or 'Algorithm' block or figure. |
| Open Source Code | No | The paper states: 'Our techniques are implemented in Fast Downward (FD) (Helmert 2006)', but this refers to a system they used, not the release of their own code for the described methodology. No links or explicit statements about code availability are provided. |
| Open Datasets | Yes | We evaluate our techniques on IPC benchmarks and on the 15-puzzle domain. We ran experiments on all STRIPS benchmarks from the optimal tracks of all IPCs up to IPC 18, yielding 1797 instances from 47 domains in total. We generated a benchmark set for the 15-Puzzle by performing random walks of length up to 100 (in increments of 10) backwards from the goal, and then grouping the instances by optimal plan length. |
| Dataset Splits | No | The paper discusses 'time intervals' and a 'lookahead ratio parameter l' for balancing lookahead and refinement, but it does not specify traditional training/validation/test dataset splits. |
| Hardware Specification | Yes | The experiments are run on a cluster of Intel Xeon E5-2650 v3 machines, with a time limit of 10 minutes and memory limit of 4 GB. |
| Software Dependencies | No | The paper states 'Our techniques are implemented in Fast Downward (FD) (Helmert 2006), building upon the implementation of Cartesian abstraction heuristics (Seipp and Helmert 2018) and its online-refinement framework (Eifler and Fickert 2018).' While specific software is mentioned, no version numbers are provided for these dependencies, only publication years for related work. |
| Experiment Setup | Yes | We use the notation hb for the configurations using Bellman updates, hr for abstraction refinement, and hr+b for the combination of refinement with Bellman updates. We use actual CPU time instead of node expansions to demarcate each time slot. The lookahead ratio parameter l, which allows for a tradeoff between lookahead and refinement. For the remaining experiments, we use the overall best performing value of l = 0.8 for hr. We use l = 0.7 for hr+b, which we determined from similar experiments. with time steps of 0.01 and 0.1 seconds. time limit of 10 minutes and memory limit of 4 GB. |