用主动推理实现无需人工干预的持续学习，可自适应复杂环境。
Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active Inference
- 基于主动推理构建离散时间持续学习框架，融合感知与决策。
- 在动态环境中高效重学并优化模型，无需外部干预。
- 适合金融、医疗等需持续适应的复杂场景应用。
主动推理是一种数学框架，用于理解生物或人工代理如何与环境交互，支持持续适应与决策。它结合贝叶斯推断与自由能最小化，建模不确定且动态环境中的感知、行动与学习。与强化学习不同，主动推理通过最小化预期自由能，自然融合探索与利用。本文提出一个基于主动推理的离散时间持续学习框架，推导变分自由能与预期自由能的数学表达式，并用于设计一个自学习研究代理。该代理可根据新数据自动更新信念并调整行为，无需人工干预。在变化环境中的实验表明，其具备高效重学与模型精炼能力，适用于金融、医疗等复杂领域。论文最后讨论该框架的泛化潜力，表明主动推理是适应性人工智能的一种灵活方法。
原文摘要 · Abstract (English)
Active inference is a mathematical framework for understanding how agents (biological or artificial) interact with their environments, enabling continual adaptation and decision-making. It combines Bayesian inference and free energy minimization to model perception, action, and learning in uncertain and dynamic contexts. Unlike reinforcement learning, active inference integrates exploration and exploitation seamlessly by minimizing expected free energy. In this paper, we present a continual learning framework for agents operating in discrete time environments, using active inference as the foundation. We derive the mathematical formulations of variational and expected free energy and apply them to the design of a self-learning research agent. This agent updates its beliefs and adapts its actions based on new data without manual intervention. Through experiments in changing environments, we demonstrate the agent's ability to relearn and refine its models efficiently, making it suitable for complex domains like finance and healthcare. The paper concludes by discussing how the proposed framework generalizes to other systems, positioning active inference as a flexible approach for adaptive AI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。