Identification and Formalization of Human-Machine Collaboration Patterns, data underlying the publication: Ontology-based Reflective Communication for Shared Human-AI Recognition of Emergent Collaboration Patterns

When humans and AI-agents collaborate, they need to continuously learn about each other and the task. We propose a Team Design Pattern that utilizes adaptivity in the behavior of human and agent team partners, causing new Collaboration Patterns to emerge. Human-AI Co-Learning takes place when partners can formalize recognized patterns of collaboration in a commonly shared language, and can communicate with each other about these patterns. For this, we developed an ontology of Collaboration Patterns. An accompanying Graphical User Interface (GUI) enables partners to formalize and refine Collaboration Patterns, which can then be communicated to the partner. The dataset was gathered in an empirical evaluation with human participants who viewed video recordings of joint human-agent activities. Participants were requested to identify Collaboration Patterns in the footage, and to formalize patterns by using the ontology’s GUI. /ppbr/ppThe files contain an overview of the formalized Collaboration Patterns per participant, as well as a coding of whether they were recognized and formalized correctly and completely./ppbr/ppThe details of the research for which this data was collected and the experiment can be found here: https://doi.org/10.1007/978-3-031-21203-1_40/p

当人类与AI智能体（AI Agent）开展协作时，双方需要持续增进对彼此与协作任务的认知。我们提出了一种团队设计模式，该模式通过利用人类与智能体协作伙伴的行为适应性，催生全新的协作模式。当协作双方能够以通用共享语言将已识别的协作模式进行形式化定义，并就这些模式开展沟通时，便形成了人机协同学习（Human-AI Co-Learning）。为此，我们构建了一套协作模式本体。配套的图形用户界面（GUI）可协助协作双方对协作模式进行形式化定义与优化，随后可将这些模式传递给协作伙伴。

本数据集文件包含各参与者形式化定义后的协作模式汇总信息，同时附带了对这些模式是否被正确且完整地识别并完成形式化定义的编码标注。

本数据集所属的研究细节与实验详情可通过以下链接获取：https://doi.org/10.1007/978-3-031-21203-1_40/p