Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Online Learning of Action Models for PDDL Planning
Authors: Leonardo Lamanna, Alessandro Saetti, Luciano Serafini, Alfonso Gerevini, Paolo Traverso
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show some fundamental theoretical properties of the algorithm, and we experimentally evaluate the online learning of the actions models over a large set of IPC domains. and We also provide substantial empirical evidence of the good learning performance of OLAM using a large set of benchmarks from the International Planning Competitions (IPCs). |
| Researcher Affiliation | Collaboration | Leonardo Lamanna1,2 , Alessandro Saetti1 , Luciano Sera๏ฌni2 , Alfonso E. Gerevini1 and Paolo Traverso2 1Department of Information Engineering, University of Brescia, Italy 2Fondazione Bruno Kessler (FBK), Trento, Italy |
| Pseudocode | Yes | Algorithm 1 shows the pseudocode of the OLAM (Online Learning of Action Models). |
| Open Source Code | No | The paper does not provide a specific link to a code repository or an explicit statement about the release of its source code. |
| Open Datasets | Yes | We evaluate the effectiveness of OLAM for online learning planning domains on 23 planning domains, including the domains from the learning tracks of the past IPCs and the domains used by Aineto et al. (2019). |
| Dataset Splits | No | The paper describes generating '10 small or middle-size instances' for each domain and using them sequentially for learning, but it does not provide explicit training, validation, and test dataset splits with percentages or sample counts needed to reproduce the data partitioning in a classical machine learning sense. |
| Hardware Specification | Yes | All experiments were run on an Intel Xeon Skylake 2.3 GHz with 8 cores and 64 GB of RAM. |
| Software Dependencies | No | The paper mentions using 'FASTDOWNWARD [Helmert, 2006]' but does not provide a specific version number for this software dependency. |
| Experiment Setup | Yes | For function PLAN of Algorithm 1 (line 8), we adopt FASTDOWNWARD [Helmert, 2006] with a 60 seconds timeout. |