实现合成主动推理代理,第二部分:变异信息更新。

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Thijs van de Laar;Magnus Koudahl;Bert de Vries
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引用次数: 0

摘要

自由能原理(FEP)将(生物)代理描述为相对于其环境的生成模型最小化可变自由能(FE)。主动推理(AIF)是自由能原理的必然结果,它描述了生物体如何通过最小化预期自由能目标来探索和利用其环境。在两篇相关论文中,我们描述了通过在自由形式的福尼式因子图(FFGs)上进行消息传递来合成 AIF 的可扩展认识论方法。另一篇相关论文(本文第一部分;Koudahl 等人,2023 年)介绍了一种受限 FFG(CFFG)符号,它能直观地表示 AIF 的(广义)FE 目标。本文(第二部分)通过变分法推导了在 CFFG 上最小化(广义)FE 目标的消息传递算法。模拟贝特代理和广义 FE 代理之间的比较说明了合成 AIF 的信息传递方法如何在 T 型迷宫导航任务中诱导认识行为。将 T 型迷宫模拟扩展到学习目标统计和多代理讨价还价设置,说明了这种方法如何鼓励在其他设置中重复使用节点和更新。有了合成 AIF 代理的完整消息传递账户,就有可能在不同模型中推导和重用消息更新,并更接近合成 AIF 的工业应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Realizing Synthetic Active Inference Agents, Part II: Variational Message Updates
The free energy principle (FEP) describes (biological) agents as minimizing a variational free energy (FE) with respect to a generative model of their environment. Active inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimizing an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF by message passing on free-form Forney-style factor graphs (FFGs). A companion paper (part I of this article; Koudahl et al., 2023) introduces a constrained FFG (CFFG) notation that visually represents (generalized) FE objectives for AIF. This article (part II) derives message-passing algorithms that minimize (generalized) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalized FE agents illustrates how the message-passing approach to synthetic AIF induces epistemic behavior on a T-maze navigation task. Extension of the T-maze simulation to learning goal statistics and a multiagent bargaining setting illustrate how this approach encourages reuse of nodes and updates in alternative settings. With a full message-passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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