用生物启发的生成模型模拟智能体决策，探索记忆与预测在行为中的作用。
Simulating Biological Intelligence: Active Inference with Experiment-Informed Generative Model
- 基于主动推断理论，结合实验数据构建生物启发的生成模型。
- 模拟环境中实现智能体学习，验证记忆与预测规划的关键作用。
- 为可解释人工智能提供生物可信且可扩展的决策建模方法，适合神经科学与AI交叉研究者。
随着人工智能的快速发展，理解自主智能体中目的性行为的机制至关重要。尽管人工神经网络主导了AI的发展路径，但近期研究开始探索基于生物系统的潜力，如活体神经元网络。这类系统不仅具备高能效与数据效率，还可能催生更可解释、更符合生物学原理的模型。本文提出一种基于主动推断理论的框架，用于建模具身智能体的决策行为。通过使用实验数据引导的生成模型，我们在模拟的游戏环境中重现了生物神经实验的决策过程。结果表明，智能体能够实现学习，揭示了记忆性学习与预测性规划在智能决策中的核心作用。该工作为可解释人工智能领域提供了生物基础扎实且可扩展的行为建模方法。
原文摘要 · Abstract (English)
With recent and rapid advancements in artificial intelligence (AI), understanding the foundation of purposeful behaviour in autonomous agents is crucial for developing safe and efficient systems. While artificial neural networks have dominated the path to AI, recent studies are exploring the potential of biologically based systems, such as networks of living biological neuronal networks. Along with promises of high power and data efficiency, these systems may also inform more explainable and biologically plausible models. In this work, we propose a framework rooted in active inference, a general theory of behaviour, to model decision-making in embodied agents. Using experiment-informed generative models, we simulate decision-making processes in a simulated game-play environment, mirroring experimental setups that use biological neurons. Our results demonstrate learning in these agents, providing insights into the role of memory-based learning and predictive planning in intelligent decision-making. This work contributes to the growing field of explainable AI by offering a biologically grounded and scalable approach to understanding purposeful behaviour in agents.
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