Other Open Access
Code, data, and supplement for the IJCAI 2022 Paper "Learning and Exploiting Progress States in Greedy Best-First Search"
Ferber, Patrick;
Cohen, Liat;
Jendrik Seipp;
Thomas Keller
This directory contains the code, data sets, our produced data, and some additional results for the IJCAI 2022 paper "Learning and Exploiting Progress States in Greedy Best-First Search" by Patrick Ferber, Liat Cohen, Jendrik Seipp, and Thomas Keller.
Files
(3.5 GB)
| Name | Size | |
|---|---|---|
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ferber-et-al-ijcai2022.zip
md5:7b993e4d866ae3d1d5a6678983a6db45 |
3.5 GB | Download |
74
10
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 74 | 74 |
| Downloads | 10 | 10 |
| Data volume | 34.7 GB | 34.7 GB |
| Unique views | 68 | 68 |
| Unique downloads | 9 | 9 |
Indexed in
- Publication date:
- July 23, 2022
- DOI:
-
- Keyword(s):
- Classical Planning Search Space Search Space Topology Progress States
- Grants:
-
European Commission:
-
TAILOR - Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization (952215)
- BDE - Beyond Distance Estimates: A New Theory of Heuristics for State-Space Search (817639)
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TAILOR - Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization (952215)
- Meeting:
- 31st International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria, 23-29 July 2022
- Communities:
- License (for files):
- GNU Affero General Public License v3.0 or later