Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-30043S" target="_blank" >GA22-30043S: Víceúčelové plánování úkolů a pohybů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

8

Strana od-do

592-599

Název nakladatele

Science and Technology Publications, Lda

Místo vydání

Setúbal

Místo konání akce

Rome

Datum konání akce

24. 2. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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