Choosing a Classical Planner with Graph Neural Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913225863331840 |
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| author | Vatter, Jana Mayer, Ruben Jacobsen, Hans-Arno Samulowitz, Horst Katz, Michael |
| author_facet | Vatter, Jana Mayer, Ruben Jacobsen, Hans-Arno Samulowitz, Horst Katz, Michael |
| contents | Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, the ability to predict their performance on a given problem is of great importance. While a variety of learning methods have been employed, for classical cost-optimal planning the prevailing approach uses Graph Neural Networks (GNNs). In this work, we continue the line of work on using GNNs for online planner selection. We perform a thorough investigation of the impact of the chosen GNN model, graph representation and node features, as well as prediction task. Going further, we propose using the graph representation obtained by a GNN as an input to the Extreme Gradient Boosting (XGBoost) model, resulting in a more resource-efficient yet accurate approach. We show the effectiveness of a variety of GNN-based online planner selection methods, opening up new exciting avenues for research on online planner selection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Choosing a Classical Planner with Graph Neural Networks Vatter, Jana Mayer, Ruben Jacobsen, Hans-Arno Samulowitz, Horst Katz, Michael Artificial Intelligence Machine Learning Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, the ability to predict their performance on a given problem is of great importance. While a variety of learning methods have been employed, for classical cost-optimal planning the prevailing approach uses Graph Neural Networks (GNNs). In this work, we continue the line of work on using GNNs for online planner selection. We perform a thorough investigation of the impact of the chosen GNN model, graph representation and node features, as well as prediction task. Going further, we propose using the graph representation obtained by a GNN as an input to the Extreme Gradient Boosting (XGBoost) model, resulting in a more resource-efficient yet accurate approach. We show the effectiveness of a variety of GNN-based online planner selection methods, opening up new exciting avenues for research on online planner selection. |
| title | Choosing a Classical Planner with Graph Neural Networks |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.04874 |