Groups of Agents with a Leader

We describe simulations of groups of agents that have to reach a target in a two dimensional environment, the performance criterion being the time taken by the last agent to reach the target. If the target is within a given distance from the agent, the agent moves towards the target; otherwise it moves randomly. The simulations contrast groups with and without a leader, where a leader is a member of the group which other members of the group follow as it moves through the environment. We investigate three factors that affect group performance: (1) group size; (2) the presence or absence of an individual agent with the ability to detect targets at a greater distance than those \'visible\' to its companions; (3) the existence of a communication network among group members. The results show that, in groups without communication, leaders have a beneficial effect on group performance, especially in large groups and if the individual with better than average sensory capabilities is the leader of the group. However, in situations where group members can communicate, these results are reversed, with leaders being detrimental, rather than beneficial, to group performanceAgent Based Models, Leaders, Social Simulation, Social Structure, Communication Topologies

Who is the learder? Dynamic role allocation through communication in a population of homogeneous robots

The field of collective robotics has been raising increasing interest in the last few years. In the vast majority of works devoted to collective robotics robots play all the same function, while less attention has been paid to groups of robots with different roles (teams). In this paper we evolve a population of homogeneous robots for dynamically allocating roles through bodily and communicative interactions. Evolved solutions are not only able to efficiently decide who is the leader, but are also robust to changes in team\u27s size, demonstrating the ability of Evolutionary Robotics to find efficient and robust solution to difficult design challenges by relying on selforganizing principles. Our evolved system might be used for improving robots\u27 performance in all the cases in which robots have to accomplish collective tasks for which the presence of a leader might be useful

Robotics Attack!

No abstract availabl

Boosting effect of regular sport practice in young adults: Preliminary results on cognitive and emotional abilities

Several studies have shown that physical exercise (PE) improves behavior and cognitive functioning, reducing the risk of various neurological diseases, protecting the brain from the detrimental effects of aging, facilitating body recovery after injuries, and enhancing self-efficacy and self-esteem. Emotion processing and regulation abilities are also widely acknowledged to be key to success in sports. In this study, we aim to prove that regular participation in sports enhances cognitive and emotional functioning in healthy individuals. A sample of 60 students (mean age = 22.12; SD = 2.40; M = 30), divided into sportive and sedentary, were subjected to a neuropsychological tests battery to assess their overall cognitive abilities (Raven's Advanced Progressive Matrices, APM), verbal and graphic fluency (Word Fluency Task and modified Five Point Test, m-FPT), as well as their emotional awareness skills (Toronto Alexithymia Scale, TAS-20). Our results showed that sportive students performed better than sedentary ones in all cognitive tasks. Regarding emotional processing abilities, significant differences were found in the TAS-20 total score as well as in the Difficulty Describing Feelings (DDF) subscale and the Difficulty Identifying Feeling (DIF) subscale. Lastly, gender differences were found in the External-Oriented Thinking (EOT) subscale. Overall, our findings evidence that PE has positive effects on cognitive functioning and emotion regulation, suggesting how sports practice can promote mental health and wellbeing

Equal but different: Task allocation in homogeneous communicating robots

Social animals have conquered the world thanks to their ability to team up in order to solve survival problems. From ants to human beings, animals show the ability to cooperate, communicate and divide labor among individuals. Cooperation allows members of a group to solve problems that a single individual could not, or to speed up a solution by splitting a task into subparts. Biological and swarm robotics studies suggest that division of labor can be favored by differences in local information, especially in clonal individuals. However, environmental information alone could not suffice despite a task requires a role differentiation to be solved. In order to overcome this problem, in this paper, we analyze and discuss the role of a communication system able to differentiate signals emitted among groups of homogeneous robots equipped with three neurocontrollers of increasing complexity, to foster the evolution of a successful role allocation strategy in a context in which local information is not enough. Moreover, emerged behaviors suggest a relation between the complexity of neural networks and the cognitive complexity of the communication strategies used to carry out role allocation