Neural Networks as Mechanisms to Regulate Division of Labor

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. 26, 2011; Accepted October 27, 2011; Electronically published January 26, 2012 Online enhancements: appendixes abstract: In social insects, workers perform a multitude of tasks, such as foraging, nest construction, and brood rearing, without central control of how work is allocated among individuals. It has been suggested that workers choose a task by responding to stimuli gathered from the environment. Response-threshold models assume that individuals in a colony vary in the stimulus intensity (response threshold) at which they begin to perform the corresponding task. Here we highlight the limitations of these models with respect to colony performance in task allocation. First, we show with analysis and quantitative simulations that the deterministic response-threshold model constrains the workers' behavioral flexibility under some stimulus conditions. Next, we show that the probabilistic responsethreshold model fails to explain precise colony responses to varying stimuli. Both of these limitations would be detrimental to colony performance when dynamic and precise task allocation is needed. The University of Chicago Press andTo address these problems, we propose extensions of the responsethreshold model by adding variables that weigh stimuli. We test the extended response-threshold model in a foraging scenario and show in simulations that it results in an efficient task allocation. Finally, we show that response-threshold models can be formulated as artificial neural networks, which consequently provide a comprehensive framework for modeling task allocation in social insects.

Evolving Team Compositions by Agent Swapping

“…In ant colony optimization the agents traverse the edges of a graph according to established algorithms [2], [76]. In all these cases, the agents choose behaviors, tasks, actions and edges based on the values of a few control parameters that can be optimized automatically (see e.g., [77], [78]). In conclusion, our problem's formulation, although general, very well conforms to practical applications, as diverse as robotics, task allocation, video games and hyper-heuristics (i.e., searching in the space of heuristics).…”

“…If the difference between the stimulus and the agent's corresponding response thresholds was the same for all tasks, one of them was randomly chosen and performed by the agent. Second, with the extended response threshold model (ETM) [78], every agent had two thresholds corresponding to each of the two tasks and two weights corresponding to each of the two stimuli. An agent performed the task with the highest positive difference between the weighted stimulus and its own corresponding response threshold, or remained idle if both of its thresholds were higher than the weighted stimuli.…”

“…To quantify the teams' efficiency in task allocation we used a stochastic agent-based simulation to model a situation in which agents had to perform two distinct tasks [78], [90]. Our aim was to mimic situations with two vital tasks such as foraging and regulation.…”

“…G1). Note that the performance differences between the DTM and ETM are due to limitations in task switching implicitly present in the formulation of the DTM (see [78] for more details).…”

Selection methods regulate evolution of cooperation in digital evolution

“…While all five selection methods are frequently used to simulate differential selection (PSM in [19][20][21][22][23][24][25][26][27][28][29][30][31][32]; RSM in [33,34]; TPSM in [35][36][37]; TUSM in [38][39][40][41][42][43][44][45][46], TSM in [22,47,48]), the choice between them is rarely justified. Moreover, little attempt has been made to quantify the effects of selection methods on the dynamics of the digital evolution (but see [22,49]).…”