Artificially Intelligent Foraging

DSpace/Manakin Repository

Open Research Exeter (ORE)

Artificially Intelligent Foraging

Please use this identifier to cite or link to this item:


Title: Artificially Intelligent Foraging
Author: Chalk, Daniel
Advisor: Cresswell, JamesEverson, Richard
Publisher: University of Exeter
Date Issued: 2009-12-17
Abstract: Bumble bees (bombus spp.) are significant pollinators of many plants, and are particularly attracted to mass-flowering crops such as Oilseed Rape (Brassica Napus), which they cross-pollinate. B. napus is both wind and insect-pollinated, and whilst it has been found that wind is its most significant pollen vector, the influence of bumble bee pollination could be non-trivial when bee densities are large. Therefore, the assessment of pollinator-mediated cross-pollination events could be important when considering containment strategies of genetically modified (GM) crops, such as GM varieties of B. napus, but requires a landscape-scale understanding of pollinator movements, which is currently unknown for bumble bees. I developed an in silico model, entitled HARVEST, which simulates the foraging and consequential inter-patch movements of bumble bees. The model is based on principles from Reinforcement Learning and Individual Based Modelling, and uses a Linear Operator Learning Rule to guide agent learning. The model incoproates one or more agents, or bees, that learn by ‘trial-and-error’, with a gradual preference shown for patch choice actions that provide increased rewards. To validate the model, I verified its ability to replicate certain iconic patterns of bee-mediated gene flow, and assessed its accuracy in predicting the flower visits and inter-patch movement frequencies of real bees in a small-scale system. The model successfully replicated the iconic patterns, but failed to accurately predict outputs from the real system. It did, however, qualitatively replicate the high levels of inter-patch traffic found in the real small-scale system, and its quantitative discrepancies could likely be explained by inaccurate parameterisations. I also found that HARVEST bees are extremely efficient foragers, which agrees with evidence of powerful learning capabilities and risk-aversion in real bumble bees. When applying the model to the landscape-scale, HARVEST predicts that overall levels of bee-mediated gene flow are extremely low. Nonetheless, I identified an effective containment strategy in which a ‘shield’ comprised of sacrificed crops is placed between GM and conventional crop populations. This strategy could be useful for scenarios in which the tolerance for GM seed set is exceptionally low.
Type: Thesis or dissertation
Keywords: ForagingBumble beesArtificial IntelligenceReinforcement learningGene flowGM cropsHARVESTOptimal Foraging TheoryAnimal behaviour
Funders/Sponsor: BBSRC
Grant Number: BBS/Q/Q/2004/05894

Please note: Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable.

Files in this item

Files Size Format View
ChalkD.pdf 4.278Mb PDF Thumbnail
ChalkD_fm.pdf 361.0Kb PDF Thumbnail

This item appears in the following Collection(s)


My Account

Local Links