From Statistical Relational to Neurosymbolic Artificial Intelligence: a
Survey
- URL: http://arxiv.org/abs/2108.11451v4
- Date: Tue, 2 Jan 2024 07:55:58 GMT
- Title: From Statistical Relational to Neurosymbolic Artificial Intelligence: a
Survey
- Authors: Giuseppe Marra and Sebastijan Duman\v{c}i\'c and Robin Manhaeve and
Luc De Raedt
- Abstract summary: 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.
- Score: 16.080532260468594
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This survey explores the integration of learning and reasoning in two
different fields of artificial intelligence: neurosymbolic and statistical
relational artificial intelligence. Neurosymbolic artificial intelligence
(NeSy) studies the integration of symbolic reasoning and neural networks, while
statistical relational artificial intelligence (StarAI) focuses on integrating
logic with probabilistic graphical models. This survey identifies seven shared
dimensions between these two subfields of AI. These dimensions can be used to
characterize different NeSy and StarAI systems. They are concerned with (1) the
approach to logical inference, whether model or proof-based; (2) the syntax of
the used logical theories; (3) the logical semantics of the systems and their
extensions to facilitate learning; (4) the scope of learning, encompassing
either parameter or structure learning; (5) the presence of symbolic and
subsymbolic representations; (6) the degree to which systems capture the
original logic, probabilistic, and neural paradigms; and (7) the classes of
learning tasks the systems are applied to. 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.
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