Symmetry-Breaking and the Contextual Emergence of Human Multiagent Coordination and Social Activity

Richardson, Michael J.; Kallen, Rachel W.

doi:10.1142/9789814730617_0011

Cited by 14 publications

(14 citation statements)

References 73 publications

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“…Indeed, the emergence of collective behavior is often a direct consequence of human actors needing to realize task behaviors that are beyond the scope of their individual capabilities (Goldstone & Gureckis; 2009; Richardson et al, 2007). This implies that the performance ability of co-actors in relation to task difficulty reflects a generic control parameter that pushes individuals in and out of complementary and synchronous patterns of behavioral coordination (Richardson & Kallen, 2015, Roberts & Goldstone, 2011). …”

Section: Discussionmentioning

confidence: 99%

Herd Those Sheep: Emergent Multiagent Coordination and Behavioral-Mode Switching

et al. 2017

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Effectively coordinating one’s behavior with those of others is essential for successful multiagent activity. Understanding the dynamical principles that underlie such coordination has received increased attention in recent years due to a growing interest in behavioral synchrony and complex systems phenomena. Here we examined the behavioral dynamics of a novel, multiagent shepherding task, in which pairs of individuals had to contain small herds of virtual sheep to the center of a virtual game field. Initially, all pairs adopted a complementary, search and recover mode of behavioral coordination. Over the course of game play, however, a significant number of pairs spontaneously discovered a more effective coupled oscillatory containment mode of behavior. Analysis and modeling revealed that both behavioral modes were defined by the task’s underlying dynamics and, moreover, reflected context specific realizations of the lawful dynamics that define functional shepherding behavior more generally.

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Section: Discussionmentioning

confidence: 99%

Herd Those Sheep: Emergent Multiagent Coordination and Behavioral-Mode Switching

et al. 2017

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“…In turn, a growing number of researchers have also argued that multiagent activity is best conceptualized as a complex dynamical system and, moreover, that the behavioral order of self-organized, synergistic multiagent coordination can be understood and modeled using low-dimensional task or behavioral dynamics principles (e.g., Schmidt et al, 1990, 1998; Warren, 2006; Lagarde, 2013; Dumas et al, 2014; Richardson and Kallen, 2015; Richardson et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task

et al. 2017

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Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the affordance related dynamics that define an actor's choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor's hand movements when moving to grasp, relocate, or pass the object. Using a virtual reality pick and place task, we demonstrate that both the decision to pass or not pass an object and the movement trajectories of the participants can be characterized in terms of a behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight line trajectories, and non-constant velocity profiles. The proposed model highlights how the same low-dimensional behavioral dynamics can operate to constrain multiple (and often nested) levels of human activity and suggests that knowledge of what, when, where and how to move or act during pick and place behavior may be defined by these low dimensional task dynamics and, thus, can emerge spontaneously and in real-time with little a priori planning.

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“…A foundational example of such behavioral dynamics modeling is provided by the work of Fajen and Warren [11], [13], [22], [23], in which the authors successfully modeled the self-organized behavioral dynamics of human locomotory navigation and route selection. Of particular relevance for the current study, is that extensions of this model have been demonstrated to characterize human hand movements in object moving and placing task [7], [16], [18], [24], [25]. In this context an agent's end effector (i.e.…”

Section: Behavioral Dynamicsmentioning

confidence: 99%

“…Similar navigation equations can be extended to a range of complex multi-agent locomotion or pedestrian tasks [14], [15] as well as a wide range of joint action and multi-agent movement coordination tasks [7], [16], [18], [24]. In the context of robotics it has been shown that this approach to individual path navigation results in a system that is capable of avoiding local minima within a navigation space [27] as well as is capable of successfully avoiding an arbitrary number of obstacles [27], [29].…”

Section: Behavioral Dynamicsmentioning

confidence: 99%

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et al. 2017

Proceedings of the 5th International Conference on Human Agent Interaction

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Humans often engage in tasks that require or are made more efficient by coordinating with other humans. The coordination involved in these tasks can be understood in terms of the behavioral and affordance dynamics of socially embedded agents engaged in joint action activities. Behavioral dynamics provide mathematical (differential equation) models of human behavior and interaction and affordance dynamics identify and model the ways that an agent's action capabilities evolve over time. Taken together, models of human joint-action based on these approaches may provide a basis for developing robust, natural, and easy to engage artificial agents. In this paper we introduce behavioral and affordance dynamics models of human joint action in a pick-and-place task. Based on these models we provide a proof of concept pick-and-place artificial agent and implement the agent in a 3D virtual environment to interact with human co-actors.

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Symmetry-Breaking and the Contextual Emergence of Human Multiagent Coordination and Social Activity

Cited by 14 publications

References 73 publications

Herd Those Sheep: Emergent Multiagent Coordination and Behavioral-Mode Switching

Herd Those Sheep: Emergent Multiagent Coordination and Behavioral-Mode Switching

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task

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