A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making Núñez Molina, Carlos Mesejo Santiago, Pablo Fernández Olivares, Juan In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this article reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic, or a combination. Additionally, it also covers methods for learning the SDP structure. Finally, we compare the advantages and drawbacks of the existing methods and conclude that neurosymbolic AI poses a promising approach for SDM, since it combines AP and RL with a hybrid knowledge representation. 2024-10-28T08:08:25Z 2024-10-28T08:08:25Z 2024-06-28 journal article Núñez Molina, C. & Mesejo Santiago, P. & Fernández Olivares, J. ACM Comput. Surv. 56, 11, Article 272, 36 pages. [https://doi.org/10.1145/3663366] https://hdl.handle.net/10481/96375 10.1145/3663366 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Association for Computing Machinery