The ability to continuously learn and adapt to new situations is one where
humans are far superior compared to AI agents. We propose an approach to
knowledge transfer using behavioural strategies as a form of transferable
knowledge influenced by the human cognitive ability to develop strategies. A
strategy is defined as a partial sequence of events - where an event is both
the result of an agent's action and changes in state - to reach some predefined
event of interest. This information acts as guidance or a partial solution that
an agent can generalise and use to make predictions about how to handle unknown
observed phenomena. As a first step toward this goal, we develop a method for
extracting strategies from an agent's existing knowledge that can be applied in
multiple contexts. Our method combines observed event frequency information
with local sequence alignment techniques to find patterns of significance that
form a strategy. We show that our method can identify plausible strategies in
three environments: Pacman, Bank Heist and a dungeon-crawling video game. Our
evaluation serves as a promising first step toward extracting knowledge for
generalisation and, ultimately, transfer learning.

本文提出了一种用行为策略作为可转移知识的方法进行知识转移的方法，定义了策略，并结合观察到的事件频率信息和局部序列对齐技术来提取策略，并在三种环境下得出了可行的策略，这是提取知识进行泛化和最终的转移学习的有希望的第一步。

单智能体游戏中的策略提取

Strategy Extraction in Single-Agent Games

We mimic the cognitive ability of Human perception, based on Bayesian
hypothesis, to recognize view-based 3D objects. We consider approximate
Bayesian (Empirical Bayesian) for perceptual inference for recognition. We
essentially handle computation with perception.

该论文研究通过拟合贝叶斯假设模拟人类感知能力，以识别基于视图的 3D 物体，并实现了感知计算。

贝叶斯大脑：利用感知计算来识别三维物体

Bayesian Brain: Computation with Perception to Recognize 3D Objects

The fundamental goal of artificial intelligence (AI) is to mimic the core
cognitive activities of human. Despite tremendous success in the AI research,
most of existing methods have only single-cognitive ability. To overcome this
limitation and take a solid step towards artificial general intelligence (AGI),
we develop a foundation model pre-trained with huge multimodal data, which can
be quickly adapted for various downstream cognitive tasks. To achieve this
goal, we propose to pre-train our foundation model by self-supervised learning
with weak semantic correlation data crawled from the Internet and show that
promising results can be obtained on a wide range of downstream tasks.
Particularly, with the developed model-interpretability tools, we demonstrate
that strong imagination ability is now possessed by our foundation model. We
believe that our work makes a transformative stride towards AGI, from our
common practice of "weak or narrow AI" to that of "strong or generalized AI".

提出自我监督学习方法，用弱语义相关数据从互联网爬取数据进行预训练，快速适应不同下游任务的基础模型，该模型具有良好的想象能力，实现了从弱人工智能到强人工智能的跨越。