Respecializing swarms by forgetting reinforced thresholds

Kazakova, Vera A.; Wu, Annie S.; Sukthankar, Gita

doi:10.1007/s11721-020-00181-3

Cited by 9 publications

(7 citation statements)

References 52 publications

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“…The need for a negative reinforcement to enhance discrimination between different options or stimuli is well-known in learning theory and behavioural studies [36][37][38]. At the individual level, negative experiences modulate learning.…”

Section: Discussionmentioning

confidence: 99%

A model of resource partitioning between foraging bees based on learning

et al. 2021

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Central place foraging pollinators tend to develop multi-destination routes (traplines) to exploit patchily distributed plant resources. While the formation of traplines by individual pollinators has been studied in detail, how populations of foragers use resources in a common area is an open question, difficult to address experimentally. We explored conditions for the emergence of resource partitioning among traplining bees using agent-based models built from experimental data of bumblebees foraging on artificial flowers. In the models, bees learn to develop routes as a consequence of feedback loops that change their probabilities of moving between flowers. While a positive reinforcement of movements leading to rewarding flowers is sufficient for the emergence of resource partitioning when flowers are evenly distributed, the addition of a negative reinforcement of movements leading to unrewarding flowers is necessary when flowers are patchily distributed. In environments with more complex spatial structures, the negative experiences of individual bees on flowers favour spatial segregation and efficient collective foraging. Our study fills a major gap in modelling pollinator behaviour and constitutes a unique tool to guide future experimental programs.

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Section: Discussionmentioning

confidence: 99%

A model of resource partitioning between foraging bees based on learning

et al. 2021

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“…The need for a negative reinforcement to enhance discrimination between different options or stimuli is well-known in learning theory and behavioural studies (Beshers and Fewell 2001; Garrison et al . 2018; Kazakova et al . 2020).…”

Section: Discussionmentioning

confidence: 99%

“…The need of negative reinforcement rules to enhance discrimination between different options or stimuli is well known in both learning theory and behavioral studies (Beshers and Fewell 2001; Garrison et al 2018; Kazakova et al 2020). This is especially notable in collective decisions making by groups of animals and robots (Sumpter 2010), where negative feedbacks enable individuals to make fast and flexible decisions in response to changing environments (Robinson et al 2005; Seeley et al 2012).…”

Section: Discussionmentioning

confidence: 99%

A model of resource partitioning between foraging bees based on positive and negative associations

Dubois

Pasquaretta

Barron

et al. 2020

Preprint

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Central place foraging pollinators tend to develop multi-destination routes (traplines) to exploit several patchily distributed plant resources. While the formation of traplines by individual pollinators has been studied in details, how populations of individuals exploit resources in a common area is an open question difficult to address experimentally. Here we explored conditions for the emergence of resource partitioning among traplining bees using agent-based models built from experimental data of bumblebees foraging on artificial flowers. In the models, bees learn to develop routes as a consequence of feedback loops that change their probabilities of moving between flowers. While a positive reinforcement of route segments leading to rewarding flowers is sufficient for the emergence of resource partitioning when flowers are evenly distributed, a negative reinforcement of route segments leading to unrewarding flowers is necessary when flowers are patchily distributed. In these more complex environments, the negative experiences of individual bees favour the spatial segregation of foragers and high levels of collective foraging efficiency.

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“…These probability thresholds are also popular in the related field of task allocation to obtain the optimal distribution of agents among all the tasks presented to the MRS. In such scenarios, the use of probability thresholds allows an agent to decide whether it should stay and continue with its current task (exploitation) or move on and attempt to perform another task within the environment (exploration) (de Lope et al, 2015;Lee and Kim, 2019;Kazakova et al, 2020;Lee et al, 2020). A list of works using such probability based metrics can be found in Table 2 for different tasks.…”

Section: Probability Based Metricsmentioning

confidence: 99%

“…Given this critical distinction between static, quasi-static, and fast-evolving tasks, we propose the following definition for the latter: a task occurring in an environment that evolves at a rate at which a single agent is unable to keep up. Examples include tracking a target that can move faster than the agents (Janosov et al, 2017;Kwa et al, 2020a), and dynamic features that lead to a notable evolution in the optimum agent allocation of a task assignment problem (Kazakova et al, 2020). For a system to effectively carry out its assigned task in a fast-moving dynamic environment, there must be some form of adjustment of the balance between exploratory and exploitative actions throughout the duration of the task.…”

Section: Introductionmentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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Respecializing swarms by forgetting reinforced thresholds

Cited by 9 publications

References 52 publications

A model of resource partitioning between foraging bees based on learning

A model of resource partitioning between foraging bees based on learning

A model of resource partitioning between foraging bees based on positive and negative associations

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

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