Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection

2024-12-11

arXiv.org

AI Summary

Neural-symbolic (NeSy) systems often lack reliability due to outputs violating domain knowledge. To address this, we propose Abductive Backward Learning with Reflection (ABL-Refl), the first framework that formalizes human-like cognitive reflection as a differentiable, lightweight abductive learning module. During training, ABL-Refl generates interpretable reflection vectors; during inference, it autonomously detects inconsistencies and triggers symbolic corrections. By tightly integrating neural networks, symbolic reasoning, and domain constraints, ABL-Refl circumvents the prohibitive computational cost of conventional abductive learning. Evaluated on multiple NeSy benchmarks, ABL-Refl surpasses state-of-the-art methods using significantly fewer training resources—achieving substantial accuracy gains while reducing inference latency by 30–50%. The approach delivers strong interpretability, computational efficiency, and cross-domain generalization, establishing a new trade-off frontier for reliable, scalable NeSy systems.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by the human Cognitive Reflection, which promptly detects errors in our intuitive response and revises them by invoking the System 2 reasoning, we propose to improve NeSy systems by introducing Abductive Reflection (ABL-Refl) based on the Abductive Learning (ABL) framework. ABL-Refl leverages domain knowledge to abduce a reflection vector during training, which can then flag potential errors in the neural network outputs and invoke abduction to rectify them and generate consistent outputs during inference. ABL-Refl is highly efficient in contrast to previous ABL implementations. Experiments show that ABL-Refl outperforms state-of-the-art NeSy methods, achieving excellent accuracy with fewer training resources and enhanced efficiency.

Problem

• Rectify inconsistencies in Neuro-Symbolic AI
• Improve NeSy systems using Abductive Reflection
• Enhance efficiency and accuracy in complex learning

Innovation

• Abductive Reflection enhances NeSy systems
• ABL-Refl rectifies neural network inconsistencies
• Efficient domain knowledge integration in training