Active Inference and Epistemic Value in Graphical Models

多模态回归主动学习的互信息下界

A Mutual Information Lower Bound for Multimodal Regression Active Learning

本文提出多模态回归主动学习的互信息下界方法，通过两个索引框架区分epistemic和aleatoric不确定性，证明其在数据集增长时趋于零，且在多模态系统基准中优于其他基线方法。

Active Learning Algorithms for Graphical Model Selection

The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years. In many applications such as sensor networks and proteomics it is often expensive to obtain samples from all the variables involved simultaneously. For instance, this might involve the synchronization of a large number of sensors or the tagging of a large number of proteins. To address this important issue, we initiate the study of a novel graphical model selection problem, where the goal is to optimize the total number of scalar samples obtained by allowin…

实现合成主动推理智能体，第二部分：变分消息更新

Realising Synthetic Active Inference Agents, Part II: Variational Message Updates

本文提出在约束Forney式因子图上通过变分消息传递实现可扩展的合成主动推理智能体，并在T迷宫导航、目标统计学习和多智能体讨价还价任务中验证其认知行为与节点复用能力。

A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling

This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has two features. First, though there are different perspectives to classify DGMs, we choose to organize this review from the perspective of graphical modeling, because the learning methods for directed DGMs and undirected DGMs are fundamentally different. Second, we differentiate mode…

作为凸马尔可夫决策过程的主动推理

Active Inference as a Convex Markov Decision Process

研究将主动推理构建为策略优化，证明其可表述为凸马尔可夫决策过程，分析了相关公式并推导MD算法，表明结合世界模型学习与策略优化能赋予主动推理执行性强化学习结构，为其奠定理论基础。

基于期望自由能的规划作为变分推断

Expected Free Energy-based Planning as Variational Inference

本文提出基于期望自由能的规划可视为变分推断，统一探索与利用，并引入复杂度项，实现资源感知的可扩展主动推断。

Chance-Constrained Active Inference

Active Inference (ActInf) is an emerging theory that explains perception and action in biological agents, in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. In contrast to prior beliefs, which constrain all realizations of a random variable, we propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation, and demonstrate how such constraints can be used as intrinsic drivers for goal-directed behavior in ActInf. …

Active Bayesian Causal Inference

Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, especially in terms of actively collected interventional data, since the causal query of interest may not require a fully-specified causal model. From a Bayesian perspective, it is also unnatural, since a causal query (e.g., the causal graph or some causal effect) can be viewed as a latent quantity subject to posterior inference -- other unobserved qua…

期望自由能作为Bethe拉格朗日量的信息约束

Expected free energy as an information constraint on the Bethe Lagrangian

该研究提出受信息约束的Bethe拉格朗日量，可恢复主动推理的期望自由能解，经实验验证其在三个任务上的性能可与EFE和Q-MDP相媲美。