From Statistical Relational to Neuro-Symbolic Artificial Intelligence
- URL: http://arxiv.org/abs/2003.08316v2
- Date: Tue, 24 Mar 2020 16:03:51 GMT
- Title: From Statistical Relational to Neuro-Symbolic Artificial Intelligence
- Authors: Luc De Raedt, Sebastijan Duman\v{c}i\'c, Robin Manhaeve, and Giuseppe
Marra
- Abstract summary: This survey identifies several parallels across seven different dimensions between these two fields.
These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
- Score: 15.092816085610513
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neuro-symbolic and statistical relational artificial intelligence both
integrate frameworks for learning with logical reasoning. This survey
identifies several parallels across seven different dimensions between these
two fields. These cannot only be used to characterize and position
neuro-symbolic artificial intelligence approaches but also to identify a number
of directions for further research.
Related papers
- Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning [11.418327158608664]
Symbolic techniques with statistical strengths is a long-standing problem in artificial intelligence.
Neuro-symbolic AI focuses on this integration where the methods are in particular neural networks.
We present the first mapping of symbolic techniques into families of frameworks based on their architectures.
arXiv Detail & Related papers (2024-10-29T14:35:59Z) - Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements [50.57072342894621]
We discuss the benefits of exploiting known data dependencies and distribution constraints for Neurosymbolic use cases.
This opens a new research agenda with general questions about Neurosymbolic background knowledge and the expressivity required of its logic.
arXiv Detail & Related papers (2024-04-30T12:09:53Z) - Complexity of Probabilistic Reasoning for Neurosymbolic Classification Techniques [6.775534755081169]
We introduce a formalism for informed supervised classification and techniques.
We then build upon this formalism to define three abstract neurosymbolic techniques based on probabilistic reasoning.
arXiv Detail & Related papers (2024-04-12T11:31:37Z) - Neurosymbolic AI and its Taxonomy: a survey [48.7576911714538]
Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and neural networks.
This survey investigates research papers in this area during recent years and brings classification and comparison between the presented models as well as applications.
arXiv Detail & Related papers (2023-05-12T19:51:13Z) - Invariants for neural automata [0.0]
We develop a formal framework for the investigation of symmetries and invariants of neural automata under different encodings.
Our work could be of substantial importance for related regression studies of real-world measurements with neurosymbolic processors.
arXiv Detail & Related papers (2023-02-04T11:40:40Z) - Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years.
NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
arXiv Detail & Related papers (2022-10-28T04:38:10Z) - Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch
IoE in Wireless Network [61.90504487270785]
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM)
A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior.
A novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE.
arXiv Detail & Related papers (2022-10-13T01:08:06Z) - Spatiotemporal Patterns in Neurobiology: An Overview for Future
Artificial Intelligence [0.0]
We argue that computational models are key tools for elucidating possible functionalities that emerge from network interactions.
Here we review several classes of models including spiking neurons, integrate and fire neurons.
We hope these studies will inform future developments in artificial intelligence algorithms as well as help validate our understanding of brain processes.
arXiv Detail & Related papers (2022-03-29T10:28:01Z) - From Statistical Relational to Neurosymbolic Artificial Intelligence: a
Survey [16.080532260468594]
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
It identifies seven shared dimensions between these two subfields of AI.
By positioning various NeSy and StarAI systems along these dimensions and pointing out similarities and differences between them, this survey contributes fundamental concepts for understanding the integration of learning and reasoning.
arXiv Detail & Related papers (2021-08-25T19:47:12Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.