Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380975" target="_blank" >RIV/68407700:21230/24:00380975 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0012375800003636" target="_blank" >https://doi.org/10.5220/0012375800003636</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0012375800003636" target="_blank" >10.5220/0012375800003636</a>
Alternative languages
Result language
angličtina
Original language name
Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning
Original language description
The connection between symbolic artificial intelligence and statistical machine learning has been explored in many ways. That includes using machine learning to learn new heuristic functions for navigating classical planning algorithms. Many approaches which target this task use different problem representations and different machine learning techniques to train estimators for navigating search algorithms to find sequential solutions to deterministic problems. In this work, we focus on one of these approaches which is the semantically layered Cellular Simultaneous Neural Network architecture (slCSRN) (Urbanovská and Komenda, 2023) used to learn heuristic for grid-based planning problems represented by the semantically layered representation. We create new problem domains for this architecture-the Tetris and Rush-Hour domains. Both do not have an explicit agent that only modifies its surroundings unlike already explored problem domains. We compare the performance of the trained slCSRN to the existing classical planning heuristics and we also provide insights into the slCSRN computation as we provide explainability analysis of the learned heuristic functions.
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/GA22-30043S" target="_blank" >GA22-30043S: Multi-Goal Task-Motion Planning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
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 the 16th International Conference on Agents and Artificial Intelligence (Volume 3)
ISBN
978-989-758-680-4
ISSN
2184-3589
e-ISSN
2184-433X
Number of pages
8
Pages from-to
592-599
Publisher name
Science and Technology Publications, Lda
Place of publication
Setúbal
Event location
Rome
Event date
Feb 24, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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