Learning Safe Numeric Action Models
Authors: Argaman Mordoch, Brendan Juba, Roni Stern
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, however, N-SAM can quickly learn a safe action model that can solve most problems in the domain. ... Experimental Results Although we do not obtain theoretical completeness guarantees for worst-case distributions, whether or not N-SAM learns applicable action models in practice is an empirical question. Thus, we implemented N-SAM and evaluated its performance on 12 benchmark domains... |
| Researcher Affiliation | Academia | Argaman Mordoch1, Brendan Juba2, Roni Stern1 1Ben Gurion University in Be er Sheva, Israel 2Washington University in St. Louis, USA |
| Pseudocode | No | The paper describes the N-SAM algorithm in prose and provides a graphical illustration (Figure 1), but it does not include formal pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | N-SAM s code is available in the link https://github.com/Search-BGU/numeric-sam. |
| Open Datasets | Yes | We implemented N-SAM and evaluated its performance on 12 benchmark domains, namely Depot, Driverlog, Zenotravel, Rovers, Satellite, Settlers, and UMT domains from the 3rd International Planning Competition (IPC3) (Long and Fox 2003), and Farmland, Counters, Plant-watering, and Sailing from (Scala et al. 2017). |
| Dataset Splits | Yes | These problems in each domain were split to train and test using a 5-fold cross-validation. |
| Hardware Specification | Yes | All experiments were run on a Linux machine with 8 cores and 16 GB of RAM. |
| Software Dependencies | Yes | To solve problems, we run two well-known planners with a timeout of 60 seconds: Metric-FF (version 2.1) (Hoffmann 2003) and ENHSP (Li et al. 2018). |
| Experiment Setup | Yes | To solve problems, we run two well-known planners with a timeout of 60 seconds: Metric-FF (version 2.1) (Hoffmann 2003) and ENHSP (Li et al. 2018). For Metric-FF, we used BFS with no cost minimization and running configuration EHC+H. For ENHSP, we used Greedy Best First Search with the MRP heuristic and helpful actions. |