scholarly journals Self-organized division of cognitive labor

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254532
Author(s):  
Edgar Andrade-Lotero ◽  
Robert L. Goldstone
Keyword(s):  
Computational Model ◽  
Division Of Labor ◽  
Group Dynamics ◽  
Clear Understanding ◽  
Multi Agent Systems ◽  
Cognitive Mechanisms ◽  
Self Organized ◽  
Potential Applications ◽  
Multi Agent ◽  
Person Game

Often members of a group benefit from dividing the group’s task into separate components, where each member specializes their role so as to accomplish only one of the components. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and communication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an iterative two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the perceived similarity between a player’s actions and the task’s focal points guided the players’ choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration.

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.

Multi-agent systems to enable Industry 4.0

2020 ◽  
Vol 68 (6) ◽  
pp. 445-458
Author(s):  
Birgit Vogel-Heuser ◽  
Matthias Seitz ◽  
Luis Alberto Cruz Salazar ◽  
Felix Gehlhoff ◽  
Alaettin Dogan ◽  
...  
Keyword(s):  
Value Creation ◽  
Industry 4.0 ◽  
Clear Understanding ◽  
Multi Agent Systems ◽  
Discussion Paper ◽  
Agent Based ◽  
Self Organized ◽  
Message Structure ◽  
Multi Agent ◽  
And Control

AbstractThe discussion paper “I4.0 language: vocabulary, message structure and semantic interaction protocols of the I4.0 language”, published by the working group “Semantics and Interaction of Industry 4.0 Components” of the GMA, also known as UAG of the AG 1 of the platform Industry 4.0 (I4.0), presented a concept for the language between I4.0 components. The main conclusion is: The increasing networking and cooperation of components enable new forms of organization and control. A clear understanding of machine interactions paves self-organized and self-optimized value creation in I4.0. Agent-based systems are an option for the realization of such I4.0 architectures. Due to their features, software agents are particularly well suited for representing I4.0 components and enabling I4.0 interactions. Agents are not only able to understand the necessary machine languages, but also the essential mechanisms for self-organization and self-optimization in value creation. The paper focuses on I4.0 scenarios described by the Platform I4.0 that describes challenges for the industry towards its digital future and demonstrates how emerging challenges in the area of I4.0 can be met with the help of agent-based systems.

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.

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

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which 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 occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

Sign in / Sign up

Export Citation Format

Share Document