HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

Coucke, Nicolas; Heinrich, Mary Katherine; Cleeremans, Axel; Dorigo, Marco

doi:10.1007/978-3-030-60376-2_13

Cited by 3 publications

(17 citation statements)

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“…In this paper, we present HuGoS-'Humans Go Swarming'-a multiuser virtual environment built to support experiments in human collective behavior. This paper is an extension of a previous conference paper (Coucke et al 2020), in which we proposed the idea of HuGoS and presented an initial proof-of-concept. In this paper, we contribute a fully developed prototype of HuGoS that is open-source 1 and ready to be used by the research community.…”

Section: Introductionmentioning

confidence: 94%

“…For direct comparison between humans and robots following the same approach, HuGoS should also support robot models as avatars. In our initial presentation of HuGoS (Coucke et al 2020), we have integrated a 3D model of an e-puck robot (Mondada et al 2009), such that each instance of the robot runs its control independently in 3D space with a 3D physics engine, roughly similar to the setup of the ARGoS multi-robot simulator (Pinciroli et al 2012). Therefore, it should be feasible to replicate state-of-the-art swarm robotics studies conducted in a robot simulator such as ARGoS, for the purpose of one-to-one comparison with human behaviors.…”

Section: Experimentation Scope For Human Swarm Intelligencementioning

confidence: 99%

“…The sections below describe the general architecture, before moving to the avatar capabilities, the data that can be captured, and eventually the types of analysis that can be conducted with the captured data. Sections "Avatar capabilities"-"Analysis types" in "Appendix 1" are largely reproduced from our previous work (Coucke et al 2020).…”

Section: Appendix 1: Detailed Features Of Hugosmentioning

confidence: 99%

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HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

et al. 2021

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Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence.

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Section: Introductionmentioning

confidence: 94%

Section: Experimentation Scope For Human Swarm Intelligencementioning

confidence: 99%

Section: Appendix 1: Detailed Features Of Hugosmentioning

confidence: 99%

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HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

et al. 2021

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“…How do the operators tend to use the swarm to align it with their goals (Walker et al, 2012;Woods & Hollnagel, 2006)? How do operators coordinate goals and actions with other agents of the socio-technical system (Coucke, Heinrich, Cleeremans, & Dorigo, 2020;Ellwart, 2011;Ellwart, Happ, Gurtner, & Rack, 2015;Klein, Woods, Bradshaw, Hoffman, & Feltovich, 2004;Tavakoli, Nalbandian, & Ayanian, 2016;Woods & Hollnagel, 2006)? How does the human operator's mental model about swarm control tend to change over time, and how should the operator be trained in swarm control (Hasbach et al, 2020)?…”

Section: Holistic Perspective On Hsimentioning

confidence: 99%

“…From the swarm perspective, which may be called swarm engineering, one must investigate the questions: What socio-technical requirements must be satisfied so that given swarm implementations can achieve their goals efficiently (Bashyal & Venayagamoorthy, 2008;Brambilla et al, 2013;Hamann, 2018;Kolling et al, 2015;Schranz et al, 2020)? How can the swarm robots coordinate goals and actions with other agents of the socio-technical system (Coucke et al, 2020;Hamann, 2018)? How can the robots' control models be updated during development (Birattari et al, 2019;Brambilla et al, 2013;Hamann, 2018;Nolfi & Floreano, 2000) and deployment?…”

Section: Holistic Perspective On Hsimentioning

confidence: 99%

The design of self-organizing human–swarm intelligence

Hasbach

Bennewitz

2021

Adaptive Behavior

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Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

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Learning from humans to build social cognition among robots

et al. 2023

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Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans. We review key social processes observed in humans that could inspire valuable capabilities in robots and propose that relevant insights from human social cognition can be obtained by studying human-controlled avatars in virtual environments that have the correct balance of embodiment and constraints. Such environments need to allow participants to engage in embodied social behaviors, for instance through situatedness and bodily involvement, but, at the same time, need to artificially constrain humans to the operational conditions of robots, for instance in terms of perception and communication. We illustrate our proposed experimental method with example setups in a multi-user virtual environment.

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HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

Cited by 3 publications

References 46 publications

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

The design of self-organizing human–swarm intelligence

Learning from humans to build social cognition among robots

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