Knowledge Tree
logic rules
Also known as: logical rule, logical rules
Facts (13)
Sources
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org Nov 7, 2024 4 facts
claimUnified representation approaches in neuro-symbolic AI may address conceptual stability problems in connectionism by constraining conceptual structures with fixed logical rules during formation and updates.
claimWhen faced with new tasks, the COOL (concept-level continual learning) method adapts by updating the neural network and modifying or extending logic rules to reflect new knowledge.
referenceSen et al. (2022) proposed an LNN-based inductive logic programming method that learns interpretable logic rules from noisy, real-world data by generating rules as output based on structured input data.
procedureThe LNN-based inductive logic programming method proposed by Sen et al. (2022) operates through the following procedure: (1) Input a knowledge base containing facts, relations, and rules describing the target structure. (2) Build an LNN network based on the template to simulate logical connectives, where each node represents an expression or logical rule. (3) Use facts in the knowledge base as training data to adjust logical operations via optimization algorithms like back propagation and gradient descent. (4) Convert the trained LNN into a set of logical rules that reflect the relationships in the input data.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 3 facts
referenceHu, Ma, Liu, Hovy, and Xing (2016) describe a method for harnessing deep neural networks using logic rules.
claimDifferentiable reasoning modules improve the correctness and compositional generalization of neural networks by ensuring outputs tend toward satisfying logical rules.
referenceA differentiable neuro-symbolic reasoning approach for large knowledge graphs, introduced in reference [126], leverages logical rules for multi-hop reasoning while using neural embeddings to generalize and handle scale.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
referenceLuo et al. (2023a) proposed 'ChatRule', a method for mining logical rules with large language models for knowledge graph reasoning, in the preprint 'Chatrule: mining logical rules with large language models for knowledge graph reasoning'.
claimKnowledge graph reasoning leverages graph structures and logical rules to infer new information or relationships from existing knowledge.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 2 facts
referenceYiqun Yao, Jiaming Xu, Jing Shi, and Bo Xu developed a method for learning to activate logic rules for textual reasoning, published in Neural Networks in 2018.
claimIn neuro-symbolic reasoning tasks, the symbolic system (including the knowledge base and logic rules) orchestrates the overall reasoning process, while the neural network acts as a subcomponent that processes raw data and interprets symbolic rules in the context of a query.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 1 fact
claimLogic rule-based knowledge reasoning aims to discover knowledge according to random walks and logic rules.
Building Better Agentic Systems with Neuro-Symbolic AI cutter.com Dec 10, 2025 1 fact
claimSymbolic AI systems, also known as traditional AI, rely on explicit human-readable symbols, logical rules, and knowledge graphs to function.