scholarly journals Nonverbal leadership emergence in walking groups

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Lombardi ◽  
William H. Warren ◽  
Mario di Bernardo
Keyword(s):  
A Priori ◽  
Team Sports ◽  
Personal Factors ◽  
Multi Agent Systems ◽  
Leadership Emergence ◽  
Emergency Evacuations ◽  
Multi Agent ◽  
Group Members ◽  
Walking Group ◽  
Specific Scenario

Abstract The mechanisms underlying the emergence of leadership in multi-agent systems are under investigation in many areas of research where group coordination is involved. Nonverbal leadership has been mostly investigated in the case of animal groups, and only a few works address the problem in human ensembles, e.g. pedestrian walking, group dance. In this paper we study the emergence of leadership in the specific scenario of a small walking group. Our aim is to propose a rigorous mathematical methodology capable of unveiling the mechanisms of leadership emergence in a human group when leader or follower roles are not designated a priori. Two groups of participants were asked to walk together and turn or change speed at self-selected times. Data were analysed using time-dependent cross correlation to infer leader-follower interactions between each pair of group members. The results indicate that leadership emergence is due both to contextual factors, such as an individual’s position in the group, and to personal factors, such as an individual’s characteristic locomotor behaviour. Our approach can easily be extended to larger groups and other scenarios such as team sports and emergency evacuations.

Second-order consensus for a class of uncertain multi-agent systems subject to input saturation

2018 ◽  
Vol 41 (7) ◽  
pp. 1957-1964 ◽  
Author(s):  
Ming-Can Fan ◽  
Miaomiao Wang
Keyword(s):  
A Priori ◽  
Input Saturation ◽  
Second Order ◽  
Control Technique ◽  
Control Input ◽  
Multi Agent Systems ◽  
State Information ◽  
Agent Systems ◽  
Barbalat’S Lemma ◽  
Multi Agent

This paper investigates the leaderless and leader-following consensus problem for a class of second-order multi-agent systems subject to input saturation, that is, the control input is required to be a priori bounded. Moreover, the control coefficients are assumed to be unavailable, which cannot be lower or upper bounded by any known constants. Distributed consensus protocols are proposed based only on agents’ own velocity state information and relative position state information among neighbouring agents and the leader. By virtue of the adaptive control technique, algebraic graph theory and Barbalat’s lemma, it is proved that the states of the multi-agent systems can achieve consensus under the assumption that the interconnection topology is undirected and connected. Finally, two simulation examples are provided to illustrate the effectiveness of the theoretical results.

scholarly journals AGENT-BASED CROWD SIMULATION CONSIDERING EMOTION CONTAGION FOR EMERGENCY EVACUATION PROBLEM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 193-196 ◽  
Author(s):  
H. Faroqi ◽  
M.-S. Mesgari
Keyword(s):  
Emergency Situation ◽  
Emergency Evacuation ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Emergency Evacuations ◽  
R Language ◽  
Simulated Model ◽  
Agent Simulation ◽  
Adults And Children ◽  
Multi Agent

During emergencies, emotions greatly affect human behaviour. For more realistic multi-agent systems in simulations of emergency evacuations, it is important to incorporate emotions and their effects on the agents. In few words, emotional contagion is a process in which a person or group influences the emotions or behavior of another person or group through the conscious or unconscious induction of emotion states and behavioral attitudes. In this study, we simulate an emergency situation in an open square area with three exits considering Adults and Children agents with different behavior. Also, Security agents are considered in order to guide Adults and Children for finding the exits and be calm. Six levels of emotion levels are considered for each agent in different scenarios and situations. The agent-based simulated model initialize with the random scattering of agent populations and then when an alarm occurs, each agent react to the situation based on its and neighbors current circumstances. The main goal of each agent is firstly to find the exit, and then help other agents to find their ways. Numbers of exited agents along with their emotion levels and damaged agents are compared in different scenarios with different initialization in order to evaluate the achieved results of the simulated model. NetLogo 5.2 is used as the multi-agent simulation framework with R language as the developing language.

scholarly journals DEVELOPMENT OF A MULTI-AGENT SYSTEM FOR THE ASSESSMENT OF ECOLOGICAL INFLUENCES OF TRANSPORT MEGAPROJECTS: TAKING INTO ACCOUNT THE INFORMATION ENVIRONMENT FACTOR

2020 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Tatyana N. Yesikova ◽  
Svetlana V. Vakhrusheva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Information Environment ◽  
Information Space ◽  
Bering Strait ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Business Entities

The paper considers the issues of accounting and reflection in multi-agent systems of the influence of the information environment, information flows on agent behavior and the assessment of consequences, including environmental ones, of decisions made by them at various stages of large-scale infrastructure projects. The information space is a priori a multidimensional dynamic environment that is continuously updated and transformed, sometimes under the primacy of the interests of individual agents or influence groups, and much less frequently from the standpoint of ensuring the viability of the economic system as a whole. A large-scale project for the construction of a transcontinental highway (TKS) through the Bering Strait was chosen as the object of study. The article provides a fairly detailed description of the groups of agents involved in the decision-making process, as well as the elements of the information space that are significant for an agent at certain stages of its activity. To model the influence of the information space on decision-making processes by agents of different hierarchy levels (business entities, managerial entities, etc.), algorithms and special procedures have been developed.

Context-sensitive reward shaping for sparse interaction multi-agent systems

2016 ◽  
Vol 31 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Yann-Michaël de Hauwere ◽  
Sam Devlin ◽  
Daniel Kudenko ◽  
Ann Nowé
Keyword(s):  
Traffic Control ◽  
A Priori ◽  
Optimal Solution ◽  
Multi Agent Systems ◽  
Global Optimal Solution ◽  
Q Learning ◽  
Context Sensitive ◽  
Reward Shaping ◽  
Multi Agent ◽  
Global Optimal

AbstractPotential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation.This paper describes the use of context-aware potential functions in a multi-agent system in which the interactions between agents are sparse. This means that, unknown to the agentsa priori, the interactions between the agents only occur sporadically in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution.We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ-learning); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of air traffic control.

Evolutionary Multi-Agent Systems: An Adaptive and Dynamic Approach to Optimization

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Optimization Design ◽  
Design Space ◽  
A Priori ◽  
Single Agent ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a virtual team of specialized strategic software agents to cooperate and evolve to adaptively search an optimization design space. Our goal is to demonstrate and understand how such dynamically evolving teams may search more effectively than any single agent or a priori set strategy. We present a core framework and methodology that has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that may occur during the optimization process. An evolutionary approach is used, but evolution occurs at the strategic rather than the solution level, where the strategies of agents in the team are the decisions for when and how to choose and alter a solution, and the agents evolve over time. As an application of this approach in a static domain, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, with each agent employing a different solution strategy, must evolve to apply the solution strategies, which are most useful given the solution set at any point in the process. We discuss the extensions to our preliminary work that will make our framework useful to the design and optimization community.

scholarly journals Improving trust and reputation assessment with dynamic behaviour

2020 ◽  
Vol 35 ◽  
Author(s):  
Caroline Player ◽  
Nathan Griffiths
Keyword(s):  
Dynamic Behaviour ◽  
A Priori ◽  
Multi Agent Systems ◽  
Sliding Windows ◽  
Future Performance ◽  
Trust And Reputation ◽  
Multi Agent ◽  
Improved Performance ◽  
Past Experiences ◽  
Over Time

Abstract Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time. Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour. In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

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