混合学习者不会遗忘：一种受脑启发的持续学习神经符号方法
Hybrid Learners Do Not Forget: A Brain-Inspired Neuro-Symbolic Approach to Continual Learning
AI总结：
本文提出受 System 1/System 2 启发的 NeSyBiCL 框架，通过神经网络与符号推理器协同实现持续学习，并在两个新组合基准上验证其抗遗忘优势。
摘要：
持续学习对于创造能够自主学习和自我提升的 AI 智能体至关重要。持续学习的一个主要挑战是学习新任务而不丢失先前已学知识。当前的持续学习方法主要侧重于为神经网络配备缓解遗忘效应的机制。受人脑中两个不同系统（System 1 和 System 2）的启发，我们提出了一种神经符号的受脑启发持续学习（Neuro-Symbolic Brain-Inspired Continual Learning，NeSyBiCL）框架，该框架结合两个子系统来解决持续学习问题：一个负责快速适应最新任务的神经网络模型，以及一个负责保留从先前任务中获得的知识的符号推理器。此外，我们设计了这些组件之间的集成机制，以促进知识从符号推理器向神经网络的迁移。我们还引入了两个组合式持续学习基准，并证明 NeSyBiCL 是有效的；与仅依赖神经架构来解决遗忘问题的持续学习方法相比，它带来了更优越的性能。
英文摘要：
Continual learning is crucial for creating AI agents that can learn and improve themselves autonomously. A primary challenge in continual learning is to learn new tasks without losing previously learned knowledge. Current continual learning methods primarily focus on enabling a neural network with mechanisms that mitigate forgetting effects. Inspired by the two distinct systems in the human brain, System 1 and System 2, we propose a Neuro-Symbolic Brain-Inspired Continual Learning (NeSyBiCL) framework that incorporates two subsystems to solve continual learning: A neural network model responsible for quickly adapting to the most recent task, together with a symbolic reasoner responsible for retaining previously acquired knowledge from previous tasks. Moreover, we design an integration mechanism between these components to facilitate knowledge transfer from the symbolic reasoner to the neural network. We also introduce two compositional continual learning benchmarks and demonstrate that NeSyBiCL is effective and leads to superior performance compared to continual learning methods that merely rely on neural architectures to address forgetting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
