Neurosymbolic AI - Why, What, and How
| dc.contributor.author | Sheth, Amit | |
| dc.contributor.author | Roy, Kaushik | |
| dc.contributor.author | Gaur, Manas | |
| dc.date.accessioned | 2023-05-22T16:05:29Z | |
| dc.date.available | 2023-05-22T16:05:29Z | |
| dc.date.issued | 2023-05-01 | |
| dc.description.abstract | Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems. | en_US |
| dc.description.sponsorship | This work was supported in part by the National Science Foundation under Grant 2133842, “EAGER: Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning.” | en_US |
| dc.description.uri | https://arxiv.org/abs/2305.00813 | en_US |
| dc.format.extent | 6 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2seaj-31gu | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2305.00813 | |
| dc.identifier.uri | http://hdl.handle.net/11603/28031 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | Neurosymbolic AI - Why, What, and How | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-5411-2230 | en_US |