Fixed Response Thresholds and the Regulation of Division of Labor in Insect Societies

Bonabeau, Eric; Théraulaz, Guy; Deneubourg, Jean‐Louis

doi:10.1006/bulm.1998.0041

Cited by 217 publications

(138 citation statements)

References 65 publications

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“…A lower response threshold for a given zone amounts to a higher likelihood of engaging in activity given a stimulus. Bonabeau, et al [7] discuss a model in which these thresholds remain fixed over time. Later they considered that a threshold for a given task decreases during time periods when that task is performed and increases otherwise [53].…”

Section: Wasp Behavior Modelmentioning

confidence: 99%

“…1 In this paper, we present a system for dynamically scheduling parallel multipurpose machines based on the natural multi-agent system of the wasp colony. A computational model of real wasp behavior [7,[52][53][54] lies at the foundation of our approach. In this model, interactions between individual wasps and the local environment (wasp-to-environment interactions) in the form of a stimulus-response mechanism govern distributed task allocation.…”

Section: Introductionmentioning

confidence: 99%

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Wasp-like Agents for Distributed Factory Coordination

Cicirello

Smith

2004

Autonomous Agents and Multi-Agent Systems

117

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Abstract. Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we present a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest.We focus specifically on the problem of configuring parallel multi-purpose machines in a factory to best satisfy product demands over time. Wasp-like computational agents that we call routing wasps act as overall machine proxies. These agents use a model of wasp task allocation behavior, coupled with a model of wasp dominance hierarchy formation, to determine which new jobs should be accepted into the machine's queue. If you view our system from a market-oriented perspective, the policies that the routing wasps independently adapt for their respective machines can be likened to policies for deciding when to bid and when not to bid for arriving jobs.We benchmark the performance of our system on the real-world problem of assigning trucks to paint booths in a simulated vehicle paintshop. The objective of this problem is to minimize the number of paint color changes accrued by the system, assuming no a priori knowledge of the color sequence or color distribution of trucks arriving in the system. We demonstrate that our system outperforms the bidding mechanism originally implemented for the problem as well as another related adaptive bidding mechanism.

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Section: Wasp Behavior Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wasp-like Agents for Distributed Factory Coordination

Cicirello

Smith

2004

Autonomous Agents and Multi-Agent Systems

117

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show abstract

“…Even if such individual finetuning occurs, foraging is allocated and inhibited at the colony level, where the high variation between individuals would overwhelm putative individual tuning. This is not problematic because the colony can still maintain a finely tuned response given a diversity of individual signalling thresholds, as has been suggested for colony-level recruitment dancing (Mattila, Burke, & Seeley, 2008), shown for pollen and nectar foraging preferences (Page, 2013) and demonstrated theoretically for general division of labour (Bonabeau, Theraulaz, & Deneubourg, 1998).…”

Section: A Simple Rule?mentioning

confidence: 99%

Honeybees tune excitatory and inhibitory recruitment signalling to resource value and predation risk

Jack-McCollough

Nieh

2015

Animal Behaviour

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Group-foraging animals can coordinate their activities by performing positive signals that increase foraging or inhibitory signals that decrease recruitment when foragers detect danger. However, it is unclear whether foragers tune their excitatory and inhibitory signalling according to food value and predation risk. We therefore studied the signals that honeybee foragers perform before and after being attacked by live predators (wasps and spiders) or a robo-predator at a nectar source. Predator attacks significantly reduced recruitment dancing and increased stop signalling, which inhibits dancing for the dangerous resource. Attack equally reduced dancing for all sucrose concentrations. However, foragers factored travel costs into their positive signalling. At the feeder with greater travel cost (100 m), bees danced less when they responded more severely to attacks. At the low travel cost feeder (1 m), there was no significant effect of attack response severity upon dancing. Attacks increased inhibitory signal production. Live and robo-predator attacks elicited 131-fold more stop signals from foragers as compared to control treatments of freshly dead predators that did not attack. However, food profitability, distance and sucrose concentration did not alter stop signalling. We suggest that this pattern may generally characterize excitatory/inhibitory signal pairs in group foraging. Foragers tune positive signalling (recruitment) to food quality and peril, and this is countered by an inhibitory signal that is tuned to danger but not resource value.

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“…Here we have chosen to use the exponential threshold function (ETF), which is the standard threshold function as proposed by Bonabeau et al [3], and is defined, for threshold θ and stimulus s, as:…”

Section: Threshold Based Algorithmsmentioning

confidence: 99%

“…Threshold-based algorithms were developed from the threshold model of task allocation in insect colonies [3,32]. In this model tasks are categorised into a set of types.…”

Section: Introductionmentioning

confidence: 99%

The effect of load on agent-based algorithms for distributed task allocation

Goldingay

Mourik

2013

Information Sciences

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Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

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Fixed Response Thresholds and the Regulation of Division of Labor in Insect Societies

Cited by 217 publications

References 65 publications

Wasp-like Agents for Distributed Factory Coordination

Wasp-like Agents for Distributed Factory Coordination

Honeybees tune excitatory and inhibitory recruitment signalling to resource value and predation risk

The effect of load on agent-based algorithms for distributed task allocation

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