scholarly journals History-Based Response Threshold Model for Division of Labor in Multi-Agent Systems

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1232 ◽  
Author(s):  
Wonki Lee ◽  
DaeEun Kim
Keyword(s):  
Division Of Labor ◽  
Threshold Model ◽  
Response Threshold ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Response Threshold Model ◽  
Multi Agent

Division of Labor and Task Allocation

1999 ◽  
Author(s):  
Eric Bonabeau ◽  
Marco Dorigo ◽  
Guy Theraulaz
Keyword(s):  
Task Performance ◽  
Division Of Labor ◽  
Social Insects ◽  
Task Allocation ◽  
Multiagent System ◽  
Threshold Model ◽  
Response Threshold ◽  
Response Threshold Model ◽  
Response Thresholds ◽  
Colony Level

Many species of social insects have a division of labor. The resilience of task allocation exhibited at the colony level is connected to the elasticity of individual workers. The behavioral repertoire of workers can be stretched back and forth in response to perturbations. A model based on response thresholds connects individual-level plasticity with colony-level resiliency and can account for some important experimental results. Response thresholds refer to likelihood of reacting to task-associated stimuli. Low-threshold individuals perform tasks at a lower level of stimulus than high-threshold individuals. An extension of this model includes a simple form of learning. Within individual workers, performing a given task induces a decrease of the corresponding threshold, and not performing the task induces an increase of the threshold. This double reinforcement process leads to the emergence of specialized workers, that is, workers that are more responsive to stimuli associated with particular task requirements, from a group of initially identical individuals. The fixed response threshold model can be used to allocate tasks in a multiagent system, in a way that is similar to market-based models, where agents bid to get resources or perform tasks. The response threshold model with learning can be used to generate differentiation in task performance in a multiagent system composed of initially identical entities. Task allocation in this case is emergent and more robust with respect to perturbations of the system than when response thresholds are fixed. An example application to distributed mail retrieval is presented. In social insects, different activities are often performed simultaneously by specialized individuals. This phenomenon is called division of labor [253, 272]. Simultaneous task performance by specialized workers is believed to be more efficient than sequential task performance by unspecialized workers [188, 253]. Parallelism avoids task switching, which costs energy and time. Specialization allows greater efficiency of individuals in task performance because they “know” the task or are better equipped for it. All social insects exhibit reproductive division of labor: only a small fraction of the colony, often limited to a single individual, reproduces.

scholarly journals Analysis of coordinated behavior structures with multi-agent deep reinforcement learning

2020 ◽  
Author(s):  
Yuki Miyashita ◽  
Toshiharu Sugawara
Keyword(s):  
Reinforcement Learning ◽  
Division Of Labor ◽  
Social Norm ◽  
The Other ◽  
Multi Agent Systems ◽  
Direct Communication ◽  
Scarce Resources ◽  
Agent Systems ◽  
Different Types ◽  
Multi Agent

Abstract Cooperation and coordination are major issues in studies on multi-agent systems because the entire performance of such systems is greatly affected by these activities. The issues are challenging however, because appropriate coordinated behaviors depend on not only environmental characteristics but also other agents’ strategies. On the other hand, advances in multi-agent deep reinforcement learning (MADRL) have recently attracted attention, because MADRL can considerably improve the entire performance of multi-agent systems in certain domains. The characteristics of learned coordination structures and agent’s resulting behaviors, however, have not been clarified sufficiently. Therefore, we focus here on MADRL in which agents have their own deep Q-networks (DQNs), and we analyze their coordinated behaviors and structures for the pickup and floor laying problem, which is an abstraction of our target application. In particular, we analyze the behaviors around scarce resources and long narrow passages in which conflicts such as collisions are likely to occur. We then indicated that different types of inputs to the networks exhibit similar performance but generate various coordination structures with associated behaviors, such as division of labor and a shared social norm, with no direct communication.

scholarly journals Evolution of self-organized division of labor in a response threshold model

2012 ◽  
Vol 66 (6) ◽  
pp. 947-957 ◽  
Author(s):  
Ana Duarte ◽  
Ido Pen ◽  
Laurent Keller ◽  
Franz J. Weissing
Keyword(s):  
Division Of Labor ◽  
Threshold Model ◽  
Response Threshold ◽  
Self Organized ◽  
Response Threshold Model

scholarly journals Decentralized Swarm Desynchronization via Inter-agent Variation for Logistical Resupply

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Annie Wu ◽  
Joseph Giordano ◽  
H. David Mathias ◽  
Arjun Pherwani
Keyword(s):  
Threshold Model ◽  
Response Threshold ◽  
Multi Agent System ◽  
Response Variable ◽  
Variable Threshold ◽  
Response Threshold Model ◽  
Threshold Distribution ◽  
Agent Interaction ◽  
Schedule Design ◽  
Multi Agent

Decentralized computational swarms have been used to simulate the workings of insect colonies or hives, often utilizing a response threshold model which underlies agent interaction with dynamic environmental stimuli. Here, we propose a logistics resupply problem in which agents must select from multiple incoming scheduled tasks that generate competing resource demands for workers. This work diverges from previous attempts toward analyzing swarm behaviors by examining relative amounts of stress placed on a multi-agent system in conjunction with two mechanisms of response: variable threshold distribution, or duration level. Further, we demonstrate changes to the general swarm performance’s dependence on paired desynchronization type and schedule design, as the result of varied swarm conditions.

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