This paper aims to develop a semi-formal design space for Human-AI
interactions, by building a set of interaction primitives which specify the
communication between users and AI systems during their interaction. We show
how these primitives can be combined into a set of interaction patterns which
can provide an abstract specification for exchanging messages between humans
and AI/ML models to carry out purposeful interactions. The motivation behind
this is twofold: firstly, to provide a compact generalisation of existing
practices, that highlights the similarities and differences between systems in
terms of their interaction behaviours; and secondly, to support the creation of
new systems, in particular by opening the space of possibilities for
interactions with models. We present a short literature review on frameworks,
guidelines and taxonomies related to the design and implementation of HAI
interactions, including human-in-the-loop, explainable AI, as well as hybrid
intelligence and collaborative learning approaches. From the literature review,
we define a vocabulary for describing information exchanges in terms of
providing and requesting particular model-specific data types. Based on this
vocabulary, a message passing model for interactions between humans and models
is presented, which we demonstrate can account for existing systems and
approaches. Finally, we build this into design patterns as mid-level constructs
that capture common interactional structures. We discuss how this approach can
be used towards a design space for Human-AI interactions that creates new
possibilities for designs as well as keeping track of implementation issues and
concerns.

该研究旨在开发半正式的设计空间，为人工智能交互建立一套规范用户与 AI 系统在交互过程中的通信的交互基元，进而将这些基元组合为一组交互模式，为人类和 AI/ML 模型之间交换信息提供抽象规范，以评估现有实践的相似性和差异性，并为新系统的创建提供可能性空间。

拆解人工智能与人类互动：从互动原理到设计空间

Unpacking Human-AI interactions: From interaction primitives to a design  space

The AI model has surpassed human players in the game of Go, and it is widely
believed that the AI model has encoded new knowledge about the Go game beyond
human players. In this way, explaining the knowledge encoded by the AI model
and using it to teach human players represent a promising-yet-challenging issue
in explainable AI. To this end, mathematical supports are required to ensure
that human players can learn accurate and verifiable knowledge, rather than
specious intuitive analysis. Thus, in this paper, we extract interaction
primitives between stones encoded by the value network for the Go game, so as
to enable people to learn from the value network. Experiments show the
effectiveness of our method.

AI 模型在围棋游戏中超越了人类选手，解释 AI 模型所编码的围棋知识并将其用于教导人类选手代表着一个具有前景但具有挑战性的问题。为此，本文提取围棋游戏价值网络所编码的棋子之间的交互原语，以使人们能够从价值网络中学习。实验表明了我们方法的有效性。

解析神经网络对弈围棋及人类学习

Explaining How a Neural Network Play the Go Game and Let People Learn

This paper strives to classify and detect the relationship between object
tubelets appearing within a video as a <subject-predicate-object> triplet.
Where existing works treat object proposals or tubelets as single entities and
model their relations a posteriori, we propose to classify and detect
predicates for pairs of object tubelets a priori. We also propose Social
Fabric: an encoding that represents a pair of object tubelets as a composition
of interaction primitives. These primitives are learned over all relations,
resulting in a compact representation able to localize and classify relations
from the pool of co-occurring object tubelets across all timespans in a video.
The encoding enables our two-stage network. In the first stage, we train Social
Fabric to suggest proposals that are likely interacting. We use the Social
Fabric in the second stage to simultaneously fine-tune and predict predicate
labels for the tubelets. Experiments demonstrate the benefit of early video
relation modeling, our encoding and the two-stage architecture, leading to a
new state-of-the-art on two benchmarks. We also show how the encoding enables
query-by-primitive-example to search for spatio-temporal video relations. Code:
this https URL

本文提出了一种通过分类和检测视频中的对象 tubelets 之间的关系来实现主语 - 谓语 - 宾语三元组的方法，通过学习所有关系中的交互原语来紧凑地表示对象 tubelets 对的编码，并展示了其在两个基准测试上的最新成果，同时展示了如何使用编码实现按原语示例查询时空关系。