Continual learning aims to enable machine learning models to continually
learn from a shifting data distribution without forgetting what has already
been learned. Such shifting distributions can be broken into disjoint subsets
of related examples; by training each member of an ensemble on a different
subset it is possible for the ensemble as a whole to achieve much higher
accuracy with less forgetting than a naive model. We address the problem of
selecting which models within an ensemble should learn on any given data, and
which should predict. By drawing on work from delegative voting we develop an
algorithm for using delegation to dynamically select which models in an
ensemble are active. We explore a variety of delegation methods and performance
metrics, ultimately finding that delegation is able to provide a significant
performance boost over naive learning in the face of distribution shifts.

连续学习旨在使机器学习模型能够在数据分布不断变化的情况下不忘记已学知识。通过训练集合中的每个成员于不同相关子集，使整个集合相比于单纯模型能够获得更高准确率且遗忘更少的问题。我们解决了在给定数据上选择哪些模型应该学习，哪些模型应该进行预测的问题。通过借鉴委托投票的工作，我们开发了一种用于动态选择集合中活动模型的委托算法。我们探讨了各种委托方法和性能指标，最终发现在面对分布变化时，委托能够显著提高性能，优于单纯学习。

液体集成选择用于持续学习

Liquid Ensemble Selection for Continual Learning

The constantly increasing capabilities of artificial intelligence (AI) open
new possibilities for human-AI collaboration. One promising approach to
leverage existing complementary capabilities is allowing humans to delegate
individual instances to the AI. However, enabling humans to delegate instances
effectively requires them to assess both their own and the AI's capabilities in
the context of the given task. In this work, we explore the effects of
providing contextual information on human decisions to delegate instances to an
AI. We find that providing participants with contextual information
significantly improves the human-AI team performance. Additionally, we show
that the delegation behavior changes significantly when participants receive
varying types of contextual information. Overall, this research advances the
understanding of human-AI interaction in human delegation and provides
actionable insights for designing more effective collaborative systems.

通过提供上下文信息，研究人员发现可以显著提高人工智能团队和人类之间的协作性，同时也改变了人们对于委派任务的行为方式，为设计更有效的协作系统提供了可行的见解。

人机协作中语境信息对人类委托行为的影响

On the Effect of Contextual Information on Human Delegation Behavior in  Human-AI collaboration

In this paper, we present results from a human-subject study designed to
explore two facets of human mental models of robots---inferred capability and
intention---and their relationship to overall trust and eventual decisions. In
particular, we examine delegation situations characterized by uncertainty, and
explore how inferred capability and intention are applied across different
tasks. We develop an online survey where human participants decide whether to
delegate control to a simulated UAV agent. Our study shows that human
estimations of robot capability and intent correlate strongly with overall
self-reported trust. However, overall trust is not independently sufficient to
determine whether a human will decide to trust (delegate) a given task to a
robot. Instead, our study reveals that estimations of robot intention,
capability, and overall trust are integrated when deciding to delegate. From a
broader perspective, these results suggest that calibrating overall trust alone
is insufficient; to make correct decisions, humans need (and use) multi-faceted
mental models when collaborating with robots across multiple contexts.

本文探讨人类与机器人的协作中，心理模型对信任与决策的影响，并通过一项在线测试发现，人们对机器人的能力和意图的估计与总体自我报告信任强相关。但要做出正确的决策，人们需要（并使用）多面向的心理模型，而不是仅仅校准总体信任水平。