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Department of Computer Science (DCS), Faculty of Electrical Engineering (FEE), Czech Technical University in Prague (CTU), Karlovo náměstí 293/13, 120 00 Prague, Czech Republic

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1 Papers

Explainability Insights to Cellular Simultaneous Recurrent Neural Networks for Classical Planning

Topics: Neural Networks; Machine Learning; Planning and Scheduling; Explainable Artificial Intelligence

Authors: Michaela Urbanovská and Antonín Komenda

Affiliation: Department of Computer Science (DCS), Faculty of Electrical Engineering (FEE), Czech Technical University in Prague (CTU), Karlovo náměstí 293/13, 120 00 Prague, Czech Republic

Digital Object Identifier: 10.5220/0012375800003636

Proceedings of ICAART 2024, ISBN: 978-989-758-680-4

Quick Abstract

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.