Exploiting Macro-Actions in Learning GPT-Based General Planning Policies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00387218" target="_blank" >RIV/68407700:21730/25:00387218 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICTAI66417.2025.00133" target="_blank" >https://doi.org/10.1109/ICTAI66417.2025.00133</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTAI66417.2025.00133" target="_blank" >10.1109/ICTAI66417.2025.00133</a>
Alternative languages
Result language
angličtina
Original language name
Exploiting Macro-Actions in Learning GPT-Based General Planning Policies
Original language description
Transformer-based architectures have revolutionized natural language processing and represent a significant promise for advancing policy learning in generalized planning tasks. In particular, PlanGPT demonstrated remarkable results in generating solution plans in different planning domains. For each domain, it is trained on a domain-specific dataset composed of randomly generated planning problems and corresponding solution plans. This work proposes a novel extension to the PlanGPT framework by incorporating macro-actions, high-level actions that encapsulate sequences of primitive actions, which are a well-established technique in classical planning known to improve planning efficiency by guiding exploration through shortcuts. Leveraging this concept, we investigate the impact of macro-actions on the learning process of PlanGPT and whether they mitigate the occurrence of violated preconditions caused by complex object relationships, particularly in domains where these interactions frequently lead to invalid plans due to precondition violations. Experimental results indicate that integrating macroactions improves coverage in several challenging domains and reduces generation time, highlighting the enhanced learning capabilities of PlanGPT when supported by macro-actions.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA25-18003S" target="_blank" >GA25-18003S: Using formal grammars and automata to acquire and verify domain control knowledge in planning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2025 IEEE 37th International Conference on Tools with Artificial Intelligence ICTAI 2025
ISBN
979-8-3315-4919-0
ISSN
2375-0197
e-ISSN
2375-0197
Number of pages
5
Pages from-to
915-919
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Athens
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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