实现合成主动推理智能体，第一部分：认知目标与图形化规范语言
Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language
AI总结：
本文提出一种在自由因子图上实现合成主动推理的方法，开发了约束型Forney式因子图（CFFG）表示法以完整描述优化问题，并推导出直接策略推断算法，在T型迷宫任务上验证了其信息寻求行为。
摘要：
自由能原理（FEP）是一个理论框架，用于描述（智能）系统如何通过最小化自由能泛函而自组织成连贯、稳定的结构。主动推理（AIF）是FEP的一个推论，具体描述了能够为未来做规划的系统（智能体）如何通过最小化包含信息寻求成分的特定自由能泛函来运作。本文是一个系列论文中的第一篇，我们在自由形式的因子图上推导出AIF的合成版本。本文重点在于推导用于AIF的自由能泛函的局部版本。这使我们能够构建一个适用于任意图形模型并与先前关于消息传递算法的工作相衔接的AIF版本。由此产生的消息在我们的配套论文中推导。我们还识别了因子图图形化表示中的一个空白。虽然因子图擅长表达生成模型，但迄今为止它们无法指定包括约束在内的完整优化问题。为解决这一问题，我们开发了约束型Forney式因子图（CFFG）表示法，它允许对变分推断目标进行完全图形化的描述。接着，我们展示了如何使用CFFG来重构先前的AIF算法以及推导新算法。后者通过推导一种允许对AIF智能体进行直接策略推断的算法来展示，从而绕过了长期阻碍AIF在工业环境中应用的可扩展性问题。我们在经典的T型迷宫任务上演示了我们的算法，并表明它再现了作为AIF标志性特征的信息寻求行为。
英文摘要：
The Free Energy Principle (FEP) is a theoretical framework for describing how (intelligent) systems self-organise into coherent, stable structures by minimising a free energy functional. Active Inference (AIF) is a corollary of the FEP that specifically details how systems that are able to plan for the future (agents) function by minimising particular free energy functionals that incorporate information seeking components. This paper is the first in a series of two where we derive a synthetic version of AIF on free form factor graphs. The present paper focuses on deriving a local version of the free energy functionals used for AIF. This enables us to construct a version of AIF which applies to arbitrary graphical models and interfaces with prior work on message passing algorithms. The resulting messages are derived in our companion paper. We also identify a gap in the graphical notation used for factor graphs. While factor graphs are great at expressing a generative model, they have so far been unable to specify the full optimisation problem including constraints. To solve this problem we develop Constrained Forney-style Factor Graph (CFFG) notation which permits a fully graphical description of variational inference objectives. We then proceed to show how CFFG's can be used to reconstruct prior algorithms for AIF as well as derive new ones. The latter is demonstrated by deriving an algorithm that permits direct policy inference for AIF agents, circumventing a long standing scaling issue that has so far hindered the application of AIF in industrial settings. We demonstrate our algorithm on the classic T-maze task and show that it reproduces the information seeking behaviour that is a hallmark feature of AIF.
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。
