Optimizing the Optimization of Planning Domains by Automatic Action Schema Splitting
Authors: Mojtaba Elahi, Jussi Rintanen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted two experiments to evaluate the effectiveness of our approach 2. In the first experiment, we sought to evaluate the contribution of each of our proposed components in solving hard-to-ground problems. In the second experiment, our goal was to understand how our approach influences the performance of planners when dealing with a broader range of problems. We chose four planners with different technologies for our experiments: LAMA (Richter and Westphal 2010), a heuristic-based planner; Maidu (Corrˆea et al. 2023b), a portfolio-based planner and the winner of IPC 23 in the classical-satisficing track; a variant of BFWS (Lipovetzky and Geffner 2017), featuring width-based search; and Powerlifted (Corrˆea et al. 2023a), the state-of-the-art planner for hard-to-ground problems. The variant of BFWS that we used in our experiment is a combination of two modes of this planner. First, the planner is run in goalcount-only mode for at most 15 minutes, then it switches to f5-initstate-relevant. Additionally, we used a slightly modified version of this planner with more efficient memory usage. In our experiments, we ran the planners with a 30 minute time limit on 2.50 GHz Intel Processor with an 8 GB memory limit. To determine the threshold ω in our approach, we roughly estimated that the upper bound for the number of ground actions that a planner can deal with is 106. Therefore, we set ω = 106 |A| , where A is the set of action schemas. Also, we considered a time limit of max(50, 500 |A| ) for the Monte Carlo search. In the report of our experimental results, we refer to our approach as EAS (Enhanced Action Splitter). |
| Researcher Affiliation | Academia | Mojtaba Elahi, Jussi Rintanen Aalto University mojtaba.elahi@aalto.fi, jussi.rintanen@aalto.fi |
| Pseudocode | No | The paper describes algorithms and methods in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at: https://github.com/melahi/enhanced-action-splitter |
| Open Datasets | Yes | We conducted our experiment on a set of hard-to-ground domains, including Pipesworld-Tankage, Genome Edit Distance (GED), Organic Synthesis-Alkene, Organic Synthesis-MIT, and Organic Synthesis-Original. [...] with a large set of domains, including hard-to-ground domains and domains from IPC 14, IPC 18, and IPC 23 |
| Dataset Splits | No | The paper mentions |
| Hardware Specification | Yes | In our experiments, we ran the planners with a 30 minute time limit on 2.50 GHz Intel Processor with an 8 GB memory limit. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies used in the experiments (e.g., Python, specific libraries or frameworks). |
| Experiment Setup | Yes | In our experiments, we ran the planners with a 30 minute time limit on 2.50 GHz Intel Processor with an 8 GB memory limit. To determine the threshold ω in our approach, we roughly estimated that the upper bound for the number of ground actions that a planner can deal with is 106. Therefore, we set ω = 106 |A| , where A is the set of action schemas. Also, we considered a time limit of max(50, 500 |A| ) for the Monte Carlo search. |