Use this URL to cite or link to this record in EThOS:
Title: Artificially intelligent foraging
Author: Chalk, Daniel
ISNI:       0000 0004 2678 9345
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Access from Institution:
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.
Supervisor: Cresswell, James ; Everson, Richard Sponsor: BBSRC (BBS/Q/Q/2004/05894)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Foraging ; Bumble bees ; Artificial Intelligence ; Reinforcement learning ; Gene flow ; GM crops ; HARVEST ; Optimal Foraging Theory ; Animal behaviour