scholarly journals Interpersonal synchronization of movement intermittency

2021 ◽  
Author(s):  
Alice Tomassini ◽  
Julien Laroche ◽  
Marco Emanuele ◽  
Giovanni Nazzaro ◽  
Nicola Petrone ◽  
...  
Keyword(s):  
Interpersonal Coordination ◽  
Correct Process ◽  
Loop Control ◽  
Sensory Data ◽  
Agent Interactions ◽  
Finger Flexion ◽  
Flexion Extension ◽  
Loop Dynamics ◽  
Multi Agent ◽  
Motor Loop

Humans manifest remarkable sensorimotor coordination abilities as showcased in the skilful performance expressed by orchestras and dance ensembles. In multi-agent interactions, sensorimotor loops that are normally involved in the control of one's own movement must accommodate also for sensory data (e.g., visual feedback) informing about others' movement to adjust performance and ultimately co-adapt to each other. Yet, a mechanistic understanding of how sensorimotor control comes into place to enable interpersonal coordination is still lacking. By examining movement intermittency, we here open a window into the dynamics of visuomotor loop control during interpersonal coordination. Specifically, we analysed submovements, i.e., recurrent (2-3 Hz) force pulses that are naturally engraved in our kinematics and deemed to reflect intrinsic intermittency in (visual-based) motor control. Participants were asked to synchronize rhythmic (0.25 Hz) finger flexion-extension movements. Besides synchronization at the common movement pace, finger velocity shows 2-3 Hz discontinuities that are consistently phase-locked between the two interacting partners. Notably, submovements alternate in a seemingly counterphase pattern, showing highest probability ~200ms before as well as after submovements generated by one's partner. Further, when the real partner is replaced by an unresponsive partner - a dot moving according to a pre-recorded human kinematics - submovements systematically follow the dot submovements, indicating that movement intermittency is causally linked between partners. These results show that submovements are actively adjusted (inter-locked) during interpersonal coordination. Visuo-motor loop dynamics of interacting individuals can thus couple to optimize synchronization of the sense-and-correct process that is required for behavioural coordination.

Beyond Citizen Participation in Planning

2010 ◽  
pp. 195-217 ◽  
Author(s):  
Domenico Camarda
Keyword(s):  
Citizen Participation ◽  
Environmental Planning ◽  
Multi Agent Systems ◽  
Formal Representation ◽  
Agent Interactions ◽  
Planning Methods ◽  
Multi Agent ◽  
Planning Processes ◽  
Continuous Feedback ◽  
Problem Setting

The new complexity of planning knowledge implies innovation of planning methods, in both substance and procedure. The development of multi-agent cognitive processes, particularly when the agents are diverse and dynamically associated to their interaction arenas, may have manifold implications. In particular, interesting aspects are scale problems of distributed interaction, continuous feedback on problem setting, language and representation (formal, informal, hybrid, etc.) differences among agents (Bousquet, Le Page, 2004). In this concern, an increasing number of experiences on multi-agent interactions are today located within the processes of spatial and environmental planning. Yet, the upcoming presence of different human agents often acting au paire with artificial agents in a social physical environment (see, e.g., with sensors or data-mining routines) often suggests the use of hybrid MAS-based approaches (Al-Kodmany, 2002; Ron, 2005). In this framework, the chapter will scan experiences on the setting up of cooperative multi-agent systems, in order to investigate the potentials of that approach on the interaction of agents in planning processes, beyond participatory planning as such. This investigation will reflect on agent roles, behaviours, actions in planning processes themselves. Also, an attempt will be carried out to put down formal representation of supporting architectures for interaction and decision making.

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

2009 ◽  
pp. 104-124 ◽  
Author(s):  
Yu Zhang ◽  
Mark Lewis ◽  
Christine Drennon ◽  
Michael Pellon ◽  
Coleman
Keyword(s):  
Decision Making ◽  
Social Structure ◽  
Social Systems ◽  
Three Dimensional ◽  
Decision Making Process ◽  
Social Environments ◽  
Cognitive Agents ◽  
Agent Interactions ◽  
Multi Agent ◽  
Cognitive Decision

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

scholarly journals Bounds and dynamics for empirical game theoretic analysis

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Karl Tuyls ◽  
Julien Perolat ◽  
Marc Lanctot ◽  
Edward Hughes ◽  
Richard Everett ◽  
...  
Keyword(s):  
Nash Equilibrium ◽  
Evolutionary Dynamics ◽  
Learning Algorithm ◽  
Agent Interactions ◽  
Approximate Nash Equilibrium ◽  
Blotto Game ◽  
Multi Agent ◽  
Payoff Structure ◽  
Game Theoretic ◽  
Colonel Blotto

AbstractThis paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex multi-agent interactions. In doing so we provide insights in the empirical meta game showing that a Nash equilibrium of the estimated meta-game is an approximate Nash equilibrium of the true underlying meta-game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the evolutionary dynamics analysis of meta-games using heuristic payoff tables (HPTs) to asymmetric games. The state-of-the-art has only considered evolutionary dynamics of symmetric HPTs in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), the dynamics of several teams of players in the capture the flag game (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the policy-space response oracle multi-agent learning algorithm.

scholarly journals When Shall I Be Empathetic? The Utility of Empathetic Parameter Estimation in Multi-Agent Interactions

2021 ◽  
Author(s):  
Yi Chen ◽  
Lei Zhang ◽  
Tanner Merry ◽  
Sunny Amatya ◽  
Wenlong Zhang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Agent Interactions ◽  
Multi Agent

Autonomous Internet of Things predictive control application based on wireless networked multi-agent topology and embedded operating system

2019 ◽  
Vol 234 (5) ◽  
pp. 577-595 ◽  
Author(s):  
Rihab Kouki ◽  
Alexandre Boe ◽  
Thomas Vantroys ◽  
Faouzi Bouani
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Predictive Control ◽  
Packet Losses ◽  
User Datagram Protocol ◽  
Loop Control ◽  
Limited Bandwidth ◽  
Multi Agent ◽  
Control Application ◽  
Internet Of Things Technology

This article investigates the problem of data-driven cooperative tracking for a class of multi-agent linear systems under imperfect wireless communication. An autonomous Internet of Things predictive control application is designed to drive a robot with one wheel. The proposed methodology has been developed using Revolutionary Internet of Things Operating System running on STM32 and radio frequency communication shields over the User Datagram Protocol. To evaluate the performance of the predictive control algorithm, the User Datagram Protocol has been used due to the high number of packet losses in the communication channel. A robust analysis of Internet of Things technology among agents, combined with a network predictive control strategy against packet loss, limited bandwidth and attack links is carried out. The main feature of this methodology is that it is possible to achieve consensus monitoring and stability of closed-loop control systems. The efficiency of the proposed design approach is demonstrated by several experimental scenarios.

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