How to behave efficiently and flexibly is a central problem for understanding
biological agents and creating intelligent embodied AI. It has been well known
that behavior can be classified as two types: reward-maximizing habitual
behavior, which is fast while inflexible; and goal-directed behavior, which is
flexible while slow. Conventionally, habitual and goal-directed behaviors are
considered handled by two distinct systems in the brain. Here, we propose to
bridge the gap between the two behaviors, drawing on the principles of
variational Bayesian theory. We incorporate both behaviors in one framework by
introducing a Bayesian latent variable called "intention". The habitual
behavior is generated by using prior distribution of intention, which is
goal-less; and the goal-directed behavior is generated by the posterior
distribution of intention, which is conditioned on the goal. Building on this
idea, we present a novel Bayesian framework for modeling behaviors. Our
proposed framework enables skill sharing between the two kinds of behaviors,
and by leveraging the idea of predictive coding, it enables an agent to
seamlessly generalize from habitual to goal-directed behavior without requiring
additional training. The proposed framework suggests a fresh perspective for
cognitive science and embodied AI, highlighting the potential for greater
integration between habitual and goal-directed behaviors.

该研究提出了一种使用变分贝叶斯理论桥接惯常性和目标导向性行为相互作用的框架，通过引入用于生成习惯性行为的意图先验分布和用于生成目标导向性行为的意图后验分布的贝叶斯潜变量，实现了两种行为的技能共享，并且其能够使代理程序轻松地从习惯性行为泛化到目标导向性行为。

习惯和目标的协同作用：行为的变分贝叶斯框架

Habits and goals in synergy: a variational Bayesian framework for  behavior

Transformer has become ubiquitous due to its dominant performance in various
NLP and image processing tasks. However, it lacks understanding of how to
generate mathematically grounded uncertainty estimates for transformer
architectures. Models equipped with such uncertainty estimates can typically
improve predictive performance, make networks robust, avoid over-fitting and
used as acquisition function in active learning. In this paper, we introduce
BayesFormer, a Transformer model with dropouts designed by Bayesian theory. We
proposed a new theoretical framework to extend the approximate variational
inference-based dropout to Transformer-based architectures. Through extensive
experiments, we validate the proposed architecture in four paradigms and show
improvements across the board: language modeling and classification,
long-sequence understanding, machine translation and acquisition function for
active learning.

介绍了一种名为 BayesFormer 的基于 Bayesian theory 设计的 Transformer 模型，该模型利用变分推断中的 dropout 扩展到 Transformer-based architectures，通过多种实验在自然语言处理、机器翻译、长序列理解和主动学习等领域展现出了卓越的性能。

BayesFormer：具有不确定性估计的 Transformer 模型

BayesFormer: Transformer with Uncertainty Estimation

Recent advances in Artificial Intelligence have produced agents that can beat
human world champions at games like Go, Starcraft, and Dota2. However, most of
these models do not seem to play in a human-like manner: People infer others'
intentions from their behaviour, and use these inferences in scheming and
strategizing. Here, using a Bayesian Theory of Mind (ToM) approach, we
investigated how much an explicit representation of others' intentions improves
performance in a cooperative game. We compared the performance of humans
playing with optimal-planning agents with and without ToM, in a cooperative
game where players have to flexibly cooperate to achieve joint goals. We find
that teams with ToM agents significantly outperform non-ToM agents when
collaborating with all types of partners: non-ToM, ToM, as well as human
players, and that the benefit of ToM increases the more ToM agents there are.
These findings have implications for designing better cooperative agents.

研究使用贝叶斯心理理论，通过比较具有 ToM 代理和非 ToM 代理的合作玩家的表现，证明 ToM 代理的存在可以显著提高所有类型合作伙伴（包括人类玩家）的合作效率，对于设计更好的合作代理有重要的意义。