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Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.

Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.

Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.

Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.

Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.

Figure 2 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593

Figure 2 The time step processes. The three parts of the time step (BeginStep, Step, EndStep) process can run in multithreaded mode for each object 1 to n, extant at that time and are separated by customisable methods for reporting or list management by the Population_Manager class.

Figure 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593

Figure 1 The current class hierarchy for beetle population managers, starting with the parent class Population_Manager_Base.

Figure 3 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593

Figure 3 Change in the maximum and minimum sizes and population numbers for two scenarios using the Theoretical1 species, N = do nothing, Rand = randomise the execution order.

How do students respond to different gestures produced by animated pedagogical agents? An investigation of attention, narrative recall, and knowledge transfer within a personalized learning context

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restrictedcc-by-4.0May 2024View details →

Supported data for manuscript "Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot"

The repository data corresponds partially to the manuscript titled "Can LLM-Augmented Autonomous Agents Cooperate? An Evaluation of Their Cooperative Capabilities through Melting Pot," submitted to IEEE Transactions on Artificial Intelligence. The dataset comprises experiments conducted with Large Language Model-Augmented Autonomous Agents (LAAs), as implemented in the ["Cooperative Agents" repository](https://github.com/Cooperative-IA/CooperativeGPT/tree/main), using substrates from the Melting Pot framework. <h3>Dataset Scope</h3> This dataset is divided into two main experiment categories: <ol> <li> Personality__experiments: <ul> <li>These focus on a single scenario (Commons Harvest) to assess various agent personalities and their cooperative dynamics.</li> </ul> </li> <li> Comparison_baselines__experiments: <ul> <li>These experiments include three distinct scenarios designed by Melting Pot: <ul> <li>Commons Harvest Open</li> <li>Externally Mushrooms</li> <li>Coins</li> </ul> </li> </ul> </li> </ol> These scenarios evaluate different cooperative and competitive behaviors among agents and are used to compare decision-making architectures of LAAs against reinforcement learning (RL) baselines. Unlike the Personality__experiments, these comparisons do not involve bots but exclusively analyze RL and LAA architectures. <h3>Scenarios and Metrics</h3> The metrics and indicators extracted from the experiments depend on the scenario being evaluated: <ol> <li> Commons Harvest Open: <ul> <li>Focus: Resource consumption and environmental impact.</li> <li>Metrics include: <ul> <li>Number of apples consumed.</li> <li>Devastation of trees (i.e., depletion of resources).</li> </ul> </li> </ul> </li> <li> Externally Mushrooms: <ul> <li>Focus: Self-interest vs. collective benefit.</li> <li>Agents consume mushrooms with different outcomes: <ul> <li>Mushrooms that benefit the individual.</li> <li>Mushrooms that benefit everyone.</li> <li>Mushrooms that benefit only others.</li> <li>Mushrooms that benefit the individual but penalize others.</li> </ul> </li> <li>Metrics evaluate trade-offs between individual gain and collective welfare.</li> </ul> </li> <li> Coins: <ul> <li>Focus: Reciprocity and fairness.</li> <li>Agents collect coins with two options: <ul> <li>Collect their own color coin for a reward.</li> <li>Collect a different color coin, which grants a reward to the agent but penalizes the other.</li> </ul> </li> <li>Metrics include reciprocity rates and the balance of mutual benefits.</li> </ul> </li> </ol> <h3>Objectives of Comparison Experiments</h3> The Comparison_baselines__experiments aim to: <ol> <li>Assess how LAAs compare to RL baselines in cooperative and competitive tasks across diverse scenarios.</li> <li>Compare decision-making architectures within LAAs, including chain-of-thought and generative approaches.</li> </ol> These experiments help evaluate the robustness of LAAs in scenarios with varying complexity and social dilemmas, providing insights into their potential applications in real-world cooperative systems. <h3>Simulation Details (Applicable to All Experiments)</h3> In each simulation: <ol> <li> Participants: <ul> <li>Experiments involve predefined numbers of LAAs or RL agents.</li> <li>No bots are included in Comparison_baselines__experiments.</li> </ul> </li> <li> Action Dynamics: <ul> <li>Each agent performs high-level actions sequentially.</li> <li>Simulations conclude either after reaching a preset maximum number of rounds (typically 100) or prematurely if the scenario's resources are fully depleted.</li> </ul> </li> <li> Metrics and Indicators: <ul> <li>Extracted metrics depend on the scenario and include measures of individual performance, collective outcomes, and agent reciprocity.</li> </ul> </li> </ol> This repository enables reproducibility and serves as a benchmark for advancing research into cooperative and competitive behaviors in LLM-based agents.

dryad28/100

COVID-19 PHAC Agent-Based Model for age-stratified intervention and herd immunity study

The PHAC agent-based model was used to explore the progression of the COVID-19 epidemic under three intervention scenarios (infection-preventing vaccination, illness-preventing vaccination and shielding) in individuals above three age thresholds (≥45, ≥55 and ≥65 years) while lifting shutdowns and physical distancing in the community. This data repository contains the PHAC modelling code in Anylogic used for this study.

opencc-zeroNov 2021View details →

Figure 2 from: Kang I, Sharkey MJ, Diaz R (2021) Revision of the genus Schoenlandella (Hymenoptera, Braconidae, Cardiochilinae) in the New World, with a potential biological control agent for a lepidopteran pest of bitter gourd (Momordica charantia L.). Journal of Hymenoptera Research 86: 47-61. https://doi.org/10.3897/jhr.86.72690

Figure 2 Schoenlandella gloriosa, non-type Honduras A lateral habitus B dorsal habitus C anterior head D ventral metasoma; arrow: median invagination on hypopygium E dorsal propodeum and mesonotum F wings.

Figure 1 from: Kang I, Sharkey MJ, Diaz R (2021) Revision of the genus Schoenlandella (Hymenoptera, Braconidae, Cardiochilinae) in the New World, with a potential biological control agent for a lepidopteran pest of bitter gourd (Momordica charantia L.). Journal of Hymenoptera Research 86: 47-61. https://doi.org/10.3897/jhr.86.72690

Figure 1 Schoenlandella diaphaniae, non-type A lateral habitus B dorsal habitus C anterior head D ventro-lateral metasoma E dorsal propodeum and mesonotum.

Figure 4 from: Kang I, Sharkey MJ, Diaz R (2021) Revision of the genus Schoenlandella (Hymenoptera, Braconidae, Cardiochilinae) in the New World, with a potential biological control agent for a lepidopteran pest of bitter gourd (Momordica charantia L.). Journal of Hymenoptera Research 86: 47-61. https://doi.org/10.3897/jhr.86.72690

Figure 4 Schoenlandella montserratensis, sp. nov. A lateral habitus B dorsal habitus C anterior head D ventral metasoma; arrow: median fold on hypopygium E dorsal propodeum and mesonotum F wings; arrow: 3r vein on forewing.

Figure 2 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 2 A Arrhenotokous female, body, dorsal view B Thelytokous female, body, dorsal view C Arrhenotokous female, right forewing D Arrhenotokous female, right fore and hind-wings E Thelytokous female, right forewing F Thelytokous female, right fore and hind-wings.

Figure 1 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 1 Diglyphus wani, arrhenotokous male A Body, dorsal view B Body, lateral view C Head and mesosoma, lateral view D Right fore and hind wing E Head, tergum and gaster, dorsal view F Head, front view G Metasoma, ventral view H Genitalia, ventral view.

Figure 5 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 5 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on ITS2 gene of the primers ITS2F/ITS2R amplification. ITS2–1 ~ ITS2–5 were indicated as the ITS2 gene haplotypes of D. wani.

Figure 4 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 4 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on ITS1 gene of the primers 18sf1/5p8sB1d amplification. ITS1–1 ~ ITS1–6 were indicated as the ITS1 gene haplotypes of D. wani.

Figure 3 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 3 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on COI gene of the primers COΙSF/COΙ2613 amplification. COI-1 ~ COI-15 were indicated as the COI gene haplotypes of D. wani.

Figure 6 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433

Figure 6 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on 28S gene of the primers D2F/D2R amplification. 28S-1 and 28S-2 were indicated as the 28S gene haplotypes of D. wani.