Joint Activity Design Heuristics for Enhancing Human-Machine Collaboration

• URL: http://arxiv.org/abs/2512.08036v1
• Date: Mon, 08 Dec 2025 20:53:57 GMT
• Title: Joint Activity Design Heuristics for Enhancing Human-Machine Collaboration
• Authors: Mohammadreza Jalaeian, Dane A. Morey, Michael F. Rayo,
• Abstract summary: Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity.<n>This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players.<n>We synthesized fourteens from relevant literature including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human-machine activity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for effective coordination among agents engaged in the joint activity. This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players. Effective joint activity requires supporting, at minimum, five primary macrocognitive functions within teams: Event Detection, Sensemaking, Adaptability, Perspective-Shifting, and Coordination. Supporting these functions is equally as important as making technologies usable. We synthesized fourteen heuristics from relevant literature including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human-machine activity.

Related papers

• Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineering [5.627981468468872]
  Socio-emotional intelligence (SEI) enhances collaboration among human teammates, but its role in human-AI collaboration remains unclear.<n>Current AI systems lack SEI capabilities that humans bring to teamwork, creating a potential gap in collaborative dynamics.<n>This study investigates how software practitioners perceive the socio-emotional gap in HAIC and what capabilities AI systems require for effective collaboration.
  arXiv  Detail & Related papers  (2026-01-27T09:20:03Z)
• Decoding Psychological States Through Movement: Inferring Human Kinesic Functions with Application to Built Environments [1.433758865948252]
  We introduce the Dyadic User Engagement DataseT dataset and an embedded kinesics recognition framework.<n>DUET captures 12 dyadic interactions spanning all five kinesic functions-emblems, illustrators, affect displays, adaptors, and regulators-across four sensing modalities and three built-environment contexts.<n>Our recognition framework infers communicative function directly from privacy-preserving skeletal motion without handcrafted action-to-function dictionaries.
  arXiv  Detail & Related papers  (2026-01-23T21:50:06Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [50.657070334404835]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
  Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
  arXiv  Detail & Related papers  (2024-09-13T13:19:48Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• From Interactive to Co-Constructive Task Learning [13.493719155524404]
  We will review current proposals for interactive task learning and discuss their main contributions. We then discuss our notion of co-construction and summarize research insights from adult-child and human-robot interactions.
  arXiv  Detail & Related papers  (2023-05-24T19:45:30Z)
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society [58.04479313658851]
  This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. We propose a novel communicative agent framework named role-playing. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems.
  arXiv  Detail & Related papers  (2023-03-31T01:09:00Z)
• A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams [0.0]
  We present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs) Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration.
  arXiv  Detail & Related papers  (2023-01-19T12:30:56Z)
• Joint Attention for Multi-Agent Coordination and Social Learning [108.31232213078597]
  We show that joint attention can be useful as a mechanism for improving multi-agent coordination and social learning. Joint attention leads to higher performance than a competitive centralized critic baseline across multiple environments. Taken together, these findings suggest that joint attention may be a useful inductive bias for multi-agent learning.
  arXiv  Detail & Related papers  (2021-04-15T20:14:19Z)
• Open Problems in Cooperative AI [21.303564222227727]
  Research aims to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms.
  arXiv  Detail & Related papers  (2020-12-15T21:39:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.