scholarly journals Emergence of self-organized amoeboid movement in a multi-agent approximation of Physarum polycephalum

2012 ◽  
Vol 7 (1) ◽  
pp. 016009 ◽  
Author(s):  
Jeff Jones ◽  
Andrew Adamatzky
Keyword(s):  
Physarum Polycephalum ◽  
Amoeboid Movement ◽  
Self Organized ◽  
Multi Agent

scholarly journals Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2642
Author(s):  
Godwin Asaamoning ◽  
Paulo Mendes ◽  
Denis Rosário ◽  
Eduardo Cerqueira
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Cooperative Behavior ◽  
Networked Control ◽  
Multi Agent Systems ◽  
Wireless Communication Network ◽  
Self Organized ◽  
Design Choice ◽  
Multi Agent ◽  
Computational Systems

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

scholarly journals Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1353
Author(s):  
Hai Sun ◽  
Lanling Hu ◽  
Wenchi Shou ◽  
Jun Wang
Keyword(s):  
Quantitative Relation ◽  
Response Patterns ◽  
Multi Agent Systems ◽  
Systems Methodology ◽  
Cellular Automata Model ◽  
Self Organized ◽  
Multi Agent ◽  
Exit Choice ◽  
Evacuation Plan ◽  
Exit Options

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.

scholarly journals Determining asymptotically large population sizes in insect swarms

2014 ◽  
Vol 11 (99) ◽  
pp. 20140710 ◽  
Author(s):  
James G. Puckett ◽  
Nicholas T. Ouellette
Keyword(s):  
Group Dynamics ◽  
Large Population ◽  
Environmental Cues ◽  
Multi Agent Systems ◽  
Strong Constraint ◽  
Agent Systems ◽  
Self Organized ◽  
Large Numbers ◽  
Population Sizes ◽  
Multi Agent

Social animals commonly form aggregates that exhibit emergent collective behaviour, with group dynamics that are distinct from the behaviour of individuals. Simple models can qualitatively reproduce such behaviour, but only with large numbers of individuals. But how rapidly do the collective properties of animal aggregations in nature emerge with group size? Here, we study swarms of Chironomus riparius midges and measure how their statistical properties change as a function of the number of participating individuals. Once the swarms contain order 10 individuals, we find that all statistics saturate and the swarms enter an asymptotic regime. The influence of environmental cues on the swarm morphology decays on a similar scale. Our results provide a strong constraint on how rapidly swarm models must produce collective states. But our findings support the feasibility of using swarms as a design template for multi-agent systems, because self-organized states are possible even with few agents.

scholarly journals Mechanisms Inducing Parallel Computation in a Model of Physarum polycephalum Transport Networks

2015 ◽  
Vol 25 (01) ◽  
pp. 1540004 ◽  
Author(s):  
Jeff Jones
Keyword(s):  
Physarum Polycephalum ◽  
Network Formation ◽  
Proximity Graphs ◽  
Complex Adaptive ◽  
Division Problems ◽  
Scaling Parameters ◽  
Angle Rotation ◽  
Multi Agent ◽  
Robotic Devices ◽  
Adaptive Behaviours

The giant amoeboid organism true slime mould Physarum polycephalum dynamically adapts its body plan in response to changing environmental conditions and its protoplasmic transport network is used to distribute nutrients within the organism. These networks are efficient in terms of network length and network resilience and are parallel approximations of a range of proximity graphs and plane division problems. The complex parallel distributed computation exhibited by this simple organism has since served as an inspiration for intensive research into distributed computing and robotics within the last decade. P. polycephalum may be considered as a spatially represented parallel unconventional computing substrate, but how can this ‘computer’ be programmed? In this paper we examine and catalogue individual low-level mechanisms which may be used to induce network formation and adaptation in a multi-agent model of P. polycephalum. These mechanisms include those intrinsic to the model (particle sensor angle, rotation angle, and scaling parameters) and those mediated by the environment (stimulus location, distance, angle, concentration, engulfment and consumption of nutrients, and the presence of simulated light irradiation, repellents and obstacles). The mechanisms induce a concurrent integration of chemoattractant and chemorepellent gradients diffusing within the 2D lattice upon which the agent population resides, stimulating growth, movement, morphological adaptation and network minimisation. Chemoattractant gradients, and their modulation by the engulfment and consumption of nutrients by the model population, represent an efficient outsourcing of spatial computation. The mechanisms may prove useful in understanding the search strategies and adaptation of distributed organisms within their environment, in understanding the minimal requirements for complex adaptive behaviours, and in developing methods of spatially programming parallel unconventional computers and robotic devices.

Lessons from a virtual slime: marginal mechanisms, minimal cognition and radical enactivism

2019 ◽  
Vol 28 (6) ◽  
pp. 453-464
Author(s):  
Lachlan Douglas Walmsley
Keyword(s):  
Physarum Polycephalum ◽  
Agent Model ◽  
Radical Enactivism ◽  
Slime Mould ◽  
Multi Agent ◽  
The Mind

Radical enactivism (REC) and similar embodied and enactive approaches to the mind deny that cognition is fundamentally representational, skull-bound and mechanistic in its organisation. In this article, I argue that modellers may still adopt a mechanistic strategy to produce explanations that are compatible with REC. This argument is scaffolded by a multi-agent model of the true slime mould Physarum polycephalum.

Sign in / Sign up

Export Citation Format

Share Document