Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in active inference based on 'planning' and 'learning from experience'. Furthermore, we also introduce a mixed model that navigates the data-complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyze the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.

翻译：鉴于人工智能的快速发展，理解智能行为的根基日益重要。主动推理作为一种普适的行为理论，为探究规划与决策中复杂性的基础提供了原则性方法。本文基于"规划"与"经验学习"两种路径，考察了主动推理中的两大决策机制。进一步地，我们引入了一种混合模型，该模型在两种策略的数据-复杂度权衡中游刃有余，融合二者优势以促成均衡决策。我们在一个需要智能体具备适应性的高难度网格世界场景中评估了所提模型。此外，该模型为分析各参数演化提供了机会，不仅贡献了宝贵见解，还为智能决策构建了可解释框架。