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Agent-based model in biology

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responded to increased inspection rate by increasing pretreatment effort, while others chose to avoid shipping to a specific port, or shopped for another port. An important result of the model results is that it can show or provide recommendations to policy makers about the point at which importers may start to shop for ports, such as the inspection rate at which port shopping is introduced and the importers associated with a certain level of pest risk or transportation cost are likely to make these changes. Another interesting outcome of the model is that when inspectors were not able to learn to respond to an importer with previously infested shipments, damage to California broccoli crops was estimated to be $ 150 million. However, when inspectors were able to increase inspection rates of importers with previous violations, damage to the California broccoli crops was reduced by approximately 12%. The model provides a mechanism to predict the introduction of invasive species from agricultural imports and their likely damage. Equally as important, the model provides policy makers and border control agencies with a tool that can be used to determine the best allocation of inspectional resources.
2498: 647:) that compete for space and resources in biofilms immersed in aquatic environments. iDynoMiCS can be used to seek to understand how individual microbial dynamics lead to emergent population- or biofilm-level properties and behaviours. Examining such formations is important in soil and river studies, dental hygiene studies, infectious disease and medical implant related infection research, and for understanding biocorrosion. An agent-based modelling paradigm was employed to make it possible to explore how each individual bacterium, of a particular species, contributes to the development of the biofilm. The initial illustration of iDynoMiCS considered how environmentally fluctuating oxygen availability affects the diversity and composition of a community of 312:
when climatic conditions become unfavorable for the beetle. The Pine Agent simulates the resistance of the host tree, specifically the Lodgepole pine tree, and monitors the state and attributes of each stand of trees. At some point in the MPB attack, the number of beetles per tree reaches the host tree capacity. When this point is reached, the beetles release a chemical to direct beetles to attack other trees. The Pine Agent models this behavior by calculating the beetle population density per stand and passes the information to the Beetle Agents. The Forest Management Agent was used, at the stand level, to simulate two common
332:. The model was executed for five time steps, each step representing a single year. Thirty simulation runs were conducted for each forest management strategy considered. The results of the simulation showed that when no management strategy was employed, the highest overall MPB infestation occurred. The results also showed that the salvage harvest management technique resulted in a 25% reduction in the number of forest strands killed by the MPB, as opposed to a 19% reduction by the sanitation harvest management strategy. In summary, the results show that the model can be used as a tool to build forest management policies. 3194: 401:, version 3.1.5. Spatial information on the location of the ports of entry, major highways, and transportation routes was included in the analysis as well as a map of California broccoli crops layered with invasive species establishment probability maps. BehaviorSpace, a software tool integrated with NetLogo, was used to test the effects of different parameters (e.g. shipment value, pretreatment cost) in the model. On average, 100 iterations were calculated at each level of the parameter being used, where an iteration represented a one-year run. 445:, such as reproduction and competition for resources which may have significant impacts on population trends. The agent-based modeling approach also allows modelers to create more generic and modular models that are more flexible and easier to maintain than modeling approaches that focus on the population as a whole. Other proposed advantages of agent-based models include realistic representation of a phenomenon of interest due to the interactions of a group of autonomous agents, and the capability to integrate quantitative variables, 1799: 3220: 3208: 661:
when plasmid transfer depends on growth. Sensitivity analysis techniques were employed that suggests parameters relating to timing (lag before plasmid transfer between agents) and spatial reach are more important for plasmid invasion into a biofilm than the receiving agents growth rate or probability of segregational loss. Further examples that use iDynoMiCS continue to be published, including use of iDynoMiCS in modelling of a
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MPB behavior; the Pine Agent represented the forest environment and tree health evolution; the Forest Management Agent represented the different management strategies. The Beetle Agent follows a series of rules to decide where to fly within the forest and to select a healthy tree to attack, feed, and breed. The MPB typically kills host trees in its natural environment in order to successfully reproduce. The beetle
2826: 389:). California is by far the largest producer of broccoli in the United States and so the concern and potential impact of an invasive species introduction through the chosen ports of entry is significant. The model also incorporated a spatially explicit damage function that was used to model invasive species damage in a realistic manner. Agent-based modeling provides the ability to analyze the behavior of 656:
cost the faster the switching the better. However, where faster switching incurs a higher cost, there is a strategy with optimal response time for any frequency of environmental fluctuations. This suggests that different types of denitrifying strategies win in different biological environments. Since this introduction the applications of iDynoMiCS continues to increase: a recent exploration of the
543:. Reproduction of the plant is not impacted by water flow, but can be influenced by animals, other plants, and humans. The plant can grow up to two meters tall, which is a limiting condition because it can only grow in certain water depths, and most of its biomass is found at the top of the plant in order to capture the most sunlight possible. The second plant agent in the model is 591:. It is a relatively shallow lake with an average depth of 1.55 meters and covers about 30 square kilometers. The lake is under eutrophication stress which means that nutrients are not a limiting factor for either of the plant agents in the model. The initial position of the plant agents in the model was randomly determined. The model was implemented using 361:
based on their own goals and objectives. The invasive species has the ability to determine if it has been released in an area containing the target crop, and to spread to adjacent plots of the target crop. The model incorporates spatial probability maps that are used to determine if an invasive species becomes established. The study focused on shipments of
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The first agent-based model is a multiscale model of mammary gland development starting with a rudimentary mammary ductal tree at the onset of puberty (during active proliferation) all the way to a full mammary gland at adulthood (when there is little proliferation). The model consists of millions of
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The results of the model showed that as inspection efforts increase, importers increase due care, or the pretreatment of shipments, and the total monetary loss of California crops decreases. The model showed that importers respond to an increase in inspection effort in different ways. Some importers
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practices influence the spatial distribution and patterns of insect population and their preferences for attacked and killed trees. Three management strategies were considered by the model: 1) no management, 2) sanitation harvest and 3) salvage harvest. In the model, the Beetle Agent represented the
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methodology that focuses on rules and interactions among the individual components or the agents of the matrix . The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for
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invasion in biofilms being one example. This study explored the hypothesis that poor plasmid spread in biofilms is caused by a dependence of conjugation on the growth rate of the plasmid donor agent. Through simulation, the paper suggests that plasmid invasion into a resident biofilm is only limited
441:. The agent-based modeling method was chosen because of its focus on the behavior of the individual agents rather than the population as a whole. The authors propose that traditional models that focus on populations as a whole do not take into account the complexity of the concurrent interactions in 731:
experiments the best. Surprisingly, radiation-induced cell inactivation by death did not contribute to increased stem cell frequency independently of the dose delivered in the model. Instead the model revealed that the combination of increased self-renewal and cell proliferation during puberty led
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iDynoMiCS has been developed by an international team of researchers in order to provide a common platform for further development of all individual-based models of microbial biofilms and such like. The model was originally the result of years of work by Laurent Lardon, Brian Merkey, and Jan-Ulrich
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actors, so three different types of importers were considered that differed in terms of commodity infection rates (high, medium, and low), pretreatment choice, and cost of transportation to the ports. The model gave predictions on inspection rates for each port of entry and importer and determined
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The agent-based model developed for the study considered three types of agents: invasive species, importers, and border enforcement agents. In the model, the invasive species can only react to their surroundings, while the importers and border enforcement agents are able to make their own decisions
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or low oxygen conditions. The study explores the hypothesis that the existence of diverse strategies of denitrification in an environment can be explained by solely assuming that faster response incurs a higher cost. The agent-based model suggests that if metabolic pathways can be switched without
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practices (sanitation and salvage) as well as the strategy where no management practice was employed. With the sanitation harvest strategy, if a stand has an infestation rate greater than a set threshold, the stand is removed as well as any healthy neighbor stand when the average size of the trees
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experiments provide insight into why children exposed to ionizing radiation have a substantially greater breast cancer risk than adults. Together, they support the hypothesis that the breast is susceptible to a transient increase in stem cell self-renewal when exposed to radiation during puberty,
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in the gland. This is important because stem cells are thought to be key targets for cancer initiation by ionizing radiation because they have the greatest long-term proliferative potential and mutagenic events persist in multiple daughter cells. Additionally, epidemiology data show that children
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The study started the simulation run with an initial population of 10,000 alate aphids distributed across a grid of 25 meter cells. The simulation results showed that there were two major population peaks, the first in early autumn due to an influx of alate immigrants and the second due to lower
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food source. The model also considered mortality among the Aphid agents, which is dependent on age, temperatures, and quality of habitat. The speed at which an Aphid agent ages is determined by the daily minimum, maximum, and mean temperatures. The model considered movement of the Aphid agents to
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feed on the inner bark of mature host trees, eventually killing them. In order for the beetles to reproduce, the host tree must be sufficiently large and have thick inner bark. The MPB outbreaks end when the food supply decreases to the point that there is not enough to sustain the population or
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exposed to ionizing radiation have a substantially greater breast cancer risk than adults. These experiments thus prompted questions about the underlying mechanism for the increase in mammary stem cells following radiation which can be explored by two agent-based models used in parallel with
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software package and was executed to simulate the growth and decay of the two different plant agents, taking into account the environmental agents previously discussed as well as interactions with other plant agents. The results of the model execution show that the population distribution of
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such as fire, disease, and insect infestation. Forest resources make up approximately 15% of the province's economy, so infestations caused by insects such as the MPB can have significant impacts on the economy. The MPB outbreaks are considered a major natural disturbance that can result in
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programming language. The model was run in daily time steps and focused on the autumn and winter seasons. Input data for the model included habitat data, daily minimum, maximum, and mean temperatures, and wind speed and direction. For the Aphid agents, age, position, and
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refers to "non-native" plants and animals that adversely affect the environments they invade. The introduction of invasive species may have environmental, economic, and ecological implications. An agent-based model can developed to evaluate the impacts of
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has a spatial pattern very similar to the GIS maps of observed distributions. The authors of the study conclude that the agent rules developed in the study are reasonable to simulate the spatial pattern of macrophyte growth in this particular lake.
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Either by excluding non-essential details or when details are not available, agent-based models can be constructed in the absence of complete knowledge of the system under study. This allows the model to be as simple and verifiable as possible.
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to stem cell enrichment. In contrast epithelial-mesenchymal transition in the model was shown to increase stem cell frequency not only in pubertal mammary glands but also in adult glands. This latter prediction, however, contradicted the
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but has a much shorter life span. The model also considered environmental and animal agents. Environmental agents considered included water flow, light penetration, and water depth. Flow conditions, although not of high importance to
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In an ecological context, agent-based modeling can be used to model the behaviour of different species such as insects infestations, other invasive species, aphids, aquatic populations, and the evolution of innate foraging behaviors.
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Through the use of the individual agents that interact locally with rules of behavior, agent-based models result in a synergy that leads to a higher level whole with much more intricate behavior than those of each individual agent.
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that presents invasive species risk with the goal of improving the allocation of enforcement resources and to provide a tool to policy makers to answer further questions concerning border enforcement and invasive species risk.
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To further evaluate self-renewal as the mechanism, a second agent-based model was created to simulate the growth dynamics of human mammary epithelial cells (containing stem/progenitor and differentiated cell subpopulations)
639:. The purpose of iDynoMiCS (individual-based Dynamics of Microbial Communities Simulator) is to simulate the growth of populations and communities of individual microbes (small unicellular organisms such as bacteria, 673:
in 2013, the development of iDynoMiCS as a tool for biological exploration continues apace, with new features being added when appropriate. From its inception, the team have committed to releasing iDynoMiCS as an
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conditions. Given these possibilities, it is important to understand how the environment and other organisms affect the growth of these aquatic plants to allow mitigation or prevention of these harmful impacts.
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Biological systems exhibit behavior that appears to be random. The probability of a particular behavior can be determined for a system as a whole and then be translated into rules for the individual agents.
547:, also a rooted aquatic plant. One major difference in the two plants is that the latter reproduces through the use of very small seeds called oospores and bulbills which are spread via the flow of water. 736:
data; irradiation of adult mammary glands did not lead to increased stem cell frequency. These simulations therefore suggested self-renewal as the primary mechanism behind pubertal stem cell increase.
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decreases. Finally, depth is important to both species of plants. As water depth increases, the light penetration decreases making it difficult for either species to survive beyond certain depths.
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experiments, the second agent-based model further confirmed that cells must extensively proliferate to observe a self-renewal dependent increase in stem/progenitor cell numbers after irradiation.
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Agent-based modeling is increasingly used to model the behaviour of individual cells within a tissue. These models are divided into on- and off-lattice models with on-lattice models such as
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Merkey BV, Lardon LA, Seoane JM, Kreft JU, Smets BF (September 2011). "Growth dependence of conjugation explains limited plasmid invasion in biofilms: an individual-based modelling study".
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the success rate of border agent inspection, not only for each port and importer but also for each potential level of pretreatment (no pretreatment, level one, level two, and level three).
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exceeded a set threshold. For the salvage harvest strategy, a stand is removed even it is not under a MPB attack if a predetermined number of neighboring stands are under a MPB attack.
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The behavior of an agent-based model is defined by the rules of its agents. Existing agent rules can be modified or new agents can be added without having to modify the entire model.
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tree, one of the most abundant commercial tree species in British Columbia. Insect outbreaks have resulted in the death of trees over areas of several thousand square kilometers.
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temperatures later in the year and a lack of immigrants. Ultimately, it is the goal of the researchers to adapt this model to simulate broader ecosystems and animal types.
1008:. 2010 International Congress on Environmental Modeling and Software. Osnabruck, Germany: International Environmental Modeling and Software Society (iEMSs). Archived from 940:. 2010 International Congress on Environmental Modeling and Software. Ottawa, Canada: International Environmental Modeling and Software Society (iEMSs). Archived from 678:
platform, encouraging collaborators to develop additional functionality that can then be merged into the next stable release. IDynoMiCS has been implemented in the
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Lardon LA, Merkey BV, Martins S, Dötsch A, Picioreanu C, Kreft JU, Smets BF (September 2011). "iDynoMiCS: next-generation individual-based modelling of biofilms".
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only grows up to 20 cm and requires very good light conditions as well as good water quality, all of which are limiting factors on the growth of the plant.
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in which they live as they may provide shelter and food for other aquatic organisms. However, they may also have harmful impacts such as the excessive growth of
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Preston DL, Mattsson A, Holmberg E, Shore R, Hildreth NG, Boice JD (August 2002). "Radiation effects on breast cancer risk: a pooled analysis of eight cohorts".
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programming language, with MATLAB and R scripts provided to analyse results. Biofilm structures that are formed in simulation can be viewed as a movie using
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mammary gland measurements. The model was then used to test the three different mechanisms to determine which one led to simulation results that matched
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hatch, they remain in the same location as their parents. The morphology of the nymphs is related to population density and the nutrient quality of the
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agents, with each agent representing a mammary stem cell, a progenitor cell, or a differentiated cell in the breast. Simulations were first run on the
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Rejniak KA (July 2007). "An immersed boundary framework for modelling the growth of individual cells: an application to the early tumour development".
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Experiments have shown that exposure to ionizing irradiation of pubertal mammary glands results in an increase in the ratio of mammary
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behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior.
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Ameden HA, Boxall PC, Cash SB, Vickers DA (2009). "An Agent-Based Model of Border Enforcement for Invasive Species Management".
2010: 1581:"Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors" 627:
models and models based on the subcellular element method. Some examples of specific applications of cell-based modeling are:
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The agent-based model developed for this study was designed to simulate the MPB attack behavior in order to evaluate how
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Tang J, Fernandez-Garcia I, Vijayakumar S, Martinez-Ruis H, Illa-Bochaca I, Nguyen DH, et al. (March 2014).
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An agent-based model can be used model the colonisation of bacteria onto a surface, leading to the formation of
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The study considered a forested area in the North-Central Interior of British Columbia of approximately 560
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occur in two separate phases, a migratory phase and a foraging phase, both of which affect the overall
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Nguyen DH, Oketch-Rabah HA, Illa-Bochaca I, Geyer FC, Reis-Filho JS, Mao JH, et al. (May 2011).
592: 453: 209:, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, 1530:
Mertens AC, Liu Q, Neglia JP, Wasilewski K, Leisenring W, Armstrong GT, et al. (October 2008).
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Wanner O, Eberl H, Morgenroth E, Noguera D, Picioreanu C, Rittmann B, van Loosdrecht M (2006).
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Exploring Forest Management Practices Using and Agent-Based Model of Forest Insect Infestations
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Several of the characteristics of agent-based models important to biological studies include:
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Newman TJ (2007). "Modeling Multicellular Structures Using the Subcellular Element Model".
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National Centre for Replacement, Refinement, and Reduction of Animals in Research (NC3Rs)
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Lawrencium supercomputer to parameterize and benchmark the model against a variety of
576:(EC) is a measure of light penetration in water. As EC increases, the growth rate of 3056: 2934: 2881: 2800: 2467: 2432: 2372: 2332: 2302: 2272: 2183: 2081: 2000: 1987: 1947: 1788: 1632: 1602: 1561: 1504: 1461: 1393: 1389: 1335: 1331: 1288: 1255: 1212: 1163: 1093: 1075: 1002:
Aphid Population Dynamics in Agricultural Landscapes: An Agent-based Simulation Model
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Individual-based Modeling of a Pseudomonas aeruginosa Biofilm with Glucose Substrate
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Kreft, with code contributions from Joao Xavier. With additional funding from the
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Osborne JM, Fletcher AG, Pitt-Francis JM, Maini PK, Gavaghan DJ (February 2017).
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model and off-lattice models such as center-based models, vertex-based models,
515: 296: 162: 119: 1798: 1447: 324:. The area consisted primarily of Lodgepole pine with smaller proportions of 3238: 3212: 3106: 3071: 2972: 2967: 2942: 2472: 2437: 2412: 2262: 2225: 1995: 1737: 1708: 1686: 1079: 895: 519: 503: 390: 345: 1578: 3198: 2977: 2957: 2861: 2599: 2482: 2402: 2362: 2352: 2322: 2076: 2050: 2040: 2017: 1898: 1606: 1565: 1508: 1465: 1397: 1339: 1259: 1216: 1167: 1097: 914: 838: 744:
after irradiation. By comparing the simulation results with data from the
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A model is proposed to study the population dynamics of two species of
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Agent-based modeling has been used to simulate attack behavior of the
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Exploring Multi-Agent Systems in Aquatic Population Dynamics Modeling
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Mammary stem cell enrichment following irradiation during puberty
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that absorbs nutrients from the soil and reproduces through root
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which primes the adult tissue to develop cancer decades later.
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Wiley Interdisciplinary Reviews. Systems Biology and Medicine
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is covered by forests that are constantly being modified by
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10.1667/0033-7587(2002)158[0220:reobcr]2.0.co;2
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is one of the aquatic plant agents in the model. It is an
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Metzcar J, Wang Y, Heiland R, Macklin P (February 2019).
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García MR, Vázquez JA, Teixeira IG, Alonso AA (2018).
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The combination of the two agent-based models and the
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Liang, Tong; Brinkman, Braden A. W. (14 March 2022).
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The area of interest in the model was a lake in the
686:files that are generated as the simulation is run. 381:. The selected invasive species of concern was the 2142:Task allocation and partitioning of social insects 452:The model was implemented in the modeling toolkit 631:Bacteria aggregation leading to biofilm formation 3236: 2267: 1415:. Fall 2011 Meeting of the APS Prairie Section, 1276:Single-Cell-Based Models in Biology and Medicine 998: 803:An G, Mi Q, Dutta-Moscato J, Vodovotz Y (2009). 930: 449:, and rule based behavior into the same model. 1411:Steffens MJ, Clement BJ, Wentworth CD (2011). 778: â€“ Software agent which acts autonomously 651:that induce the denitrification pathway under 493: 413:An agent-based model can be used to study the 2458:Stable isotope analysis in aquatic ecosystems 2168: 1640: 1051: 772: â€“ Type of autonomous entity in software 269: 183: 1313: 1311: 926: 924: 851: 798: 796: 794: 792: 408: 2523:Freshwater environmental quality parameters 707:experiments to evaluate cell inactivation, 518:of the lakes in which they live leading to 2175: 2161: 1647: 1633: 1036: 971:Canadian Journal of Agricultural Economics 352:-specific enforcement regimes for a given 190: 176: 1596: 1555: 1455: 1308: 1206: 1157: 1147: 1087: 1069: 1032: 1030: 921: 904: 894: 828: 789: 1536:Journal of the National Cancer Institute 564:, directly impact the seed dispersal of 1369: 1229: 994: 992: 3237: 2182: 1417:November 10–12, 2011, abstract #E1.006 1404: 1272: 1027: 604: 233: 2711: 2710: 2194: 2156: 1750:Patterns of self-organization in ants 1628: 845: 721:Lawrence Berkeley National Laboratory 397:The model was implemented and ran in 261:Modelling different species behaviour 1354: 989: 964: 962: 852:Politopoulos I (11 September 2007). 572:as it requires high water quality. 225: 2786:Oceanic physical-biological process 2648:List of freshwater ecoregions (WWF) 999:Evans A, Morgan D, Parry H (2004). 870: 335: 138:Distributed artificial intelligence 48:Agent-based computational economics 13: 1760:symmetry breaking of escaping ants 217: 153:Multi-agent reinforcement learning 14: 3256: 1363:Mathematical Modeling of Biofilms 959: 713:epithelial-mesenchymal transition 3219: 3218: 3206: 3192: 2824: 2496: 2313:Colored dissolved organic matter 1797: 1390:10.1111/j.1462-2920.2011.02535.x 1332:10.1111/j.1462-2920.2011.02414.x 983:10.1111/j.1744-7976.2009.01166.x 665:biofilm with glucose substrate. 251: 2658:Latin America and the Caribbean 1572: 1523: 1472: 1423: 1266: 1223: 1187:JCO Clinical Cancer Informatics 1174: 1115: 3140:Ecological values of mangroves 2683:North Pacific Subtropical Gyre 1232:Journal of Theoretical Biology 1104: 1045: 931:Perez L, Dragucevic S (2010). 555:has a higher growth rate than 242: 1: 1037:Li H, Mynett A, Qi H (2009). 782: 98:Platforms for software agents 63:Agent-based modeling software 58:Agent-based social simulation 3160:Marine conservation activism 3145:Fisheries and climate change 1721:Mixed-species foraging flock 1672:Agent-based model in biology 1654: 1419:. American Physical Society. 1285:10.1007/978-3-7643-8123-3_10 1149:10.1371/journal.pcbi.1005387 1071:10.1371/journal.pcbi.1009934 295:widespread mortality of the 53:Agent-based model in biology 7: 3155:Human impact on marine life 3032:Davidson Seamount § Ecology 2268:Aquatic population dynamics 1968:Particle swarm optimization 763: 494:Aquatic population dynamics 373:through the ports of entry 10: 3261: 1677:Collective animal behavior 1378:Environmental Microbiology 1320:Environmental Microbiology 1252:10.1016/j.jtbi.2007.02.019 1128:PLOS Computational Biology 1058:PLOS Computational Biology 608: 429:, a county located in the 270:Forest insect infestations 205:have many applications in 73:Agent-oriented programming 3186: 3125: 2997: 2933: 2895: 2842: 2833: 2822: 2771:Marine primary production 2723: 2719: 2706: 2665:List of marine ecoregions 2640: 2505: 2494: 2208: 2204: 2190: 2069: 2031: 1986: 1938: 1806: 1795: 1662: 1448:10.1016/j.ccr.2011.03.011 1365:. London: IWA Publishing. 883:Frontiers in Microbiology 506:play a vital role in the 409:Aphid population dynamics 3017:Coastal biogeomorphology 3012:Marine coastal ecosystem 2006:Self-propelled particles 896:10.3389/fmicb.2017.02626 625:immersed boundary method 158:Self-propelled particles 2925:Paradox of the plankton 2736:Diel vertical migration 2630:Freshwater swamp forest 2348:GIS and aquatic science 2196:General components and 2087:Collective intelligence 1953:Ant colony optimization 484:population distribution 280:Dendroctonus ponderosae 143:Multi-agent pathfinding 2751:Large marine ecosystem 2443:Shoaling and schooling 2107:Microbial intelligence 1767:Shoaling and schooling 663:Pseudomonas aeruginosa 574:Extinction coefficient 562:Potamogeton pectinatus 557:Potamogeton pectinatus 528:Potamogeton pectinatus 447:differential equations 387:Phyllotreta cruciferae 354:agricultural commodity 211:computational modeling 38:Multi-agent simulation 3170:Marine protected area 3097:Salt pannes and pools 2872:Marine larval ecology 2847:Census of Marine Life 2731:Deep scattering layer 2688:San Francisco Estuary 2653:Africa and Madagascar 2478:Underwater camouflage 2258:Aquatic biomonitoring 2198:freshwater ecosystems 649:denitrifying bacteria 419:bird cherry-oat aphid 379:Otay Mesa, California 2905:Marine bacteriophage 2867:Marine invertebrates 2127:Spatial organization 2092:Decentralised system 1930:Sea turtle migration 1784:Swarming (honey bee) 1199:10.1200/CCI.18.00069 383:crucifer flea beetle 375:Calexico, California 292:natural disturbances 276:mountain pine beetle 148:Multi-agent planning 2781:Ocean fertilization 2590:Trophic state index 2548:Lake stratification 2278:Aquatic respiration 2102:Group size measures 1664:Biological swarming 1548:10.1093/jnci/djn310 1493:2002RadR..158..220P 1244:2007JThBi.247..186R 1140:2017PLSCB..13E5387O 1111:BehaviorSpace Guide 605:Cell-based modeling 533:annual growth plant 415:population dynamics 234:Emergent properties 31:Multi-agent systems 3047:Intertidal wetland 3042:Intertidal ecology 2910:Marine prokaryotes 2852:Deep-sea community 2746:Iron fertilization 2669:Specific examples 2595:Upland and lowland 2513:Freshwater biology 2378:Microbial food web 2288:Aquatic toxicology 2231:Aquatic adaptation 2184:Aquatic ecosystems 2117:Predator satiation 1978:Swarm (simulation) 1973:Swarm intelligence 1948:Agent-based models 1779:Swarming behaviour 1481:Radiation Research 423:Rhopalosiphum padi 203:Agent-based models 3245:Agent-based model 3232: 3231: 3213:Oceans portal 3182: 3181: 3178: 3177: 3057:Hydrothermal vent 2993: 2992: 2882:Seashore wildlife 2713:Marine ecosystems 2702: 2701: 2698: 2697: 2468:Thermal pollution 2433:Ramsar Convention 2373:Microbial ecology 2333:Fisheries science 2273:Aquatic predation 2150: 2149: 2137:Military swarming 2082:Animal navigation 2001:Collective motion 1988:Collective motion 1855:reverse migration 1789:Swarming motility 1598:10.1002/stem.1533 1542:(19): 1368–1379. 1294:978-3-7643-8101-1 1015:on 6 October 2008 947:on 8 October 2015 776:Intelligent agent 709:dedifferentiation 617:cellular automata 611:Cell-based models 512:non-native plants 226:Modular structure 200: 199: 3252: 3222: 3221: 3215: 3211: 3210: 3201: 3199:Lakes portal 3197: 3196: 3195: 3165:Marine pollution 2857:Deep-water coral 2840: 2839: 2828: 2761:Marine chemistry 2721: 2720: 2708: 2707: 2625:Freshwater marsh 2518:Freshwater biome 2500: 2216:Acoustic ecology 2206: 2205: 2192: 2191: 2177: 2170: 2163: 2154: 2153: 1963:Crowd simulation 1940:Swarm algorithms 1911:Insect migration 1816:Animal migration 1808:Animal migration 1801: 1726:Mobbing behavior 1649: 1642: 1635: 1626: 1625: 1619: 1618: 1600: 1576: 1570: 1569: 1559: 1527: 1521: 1520: 1476: 1470: 1469: 1459: 1427: 1421: 1420: 1408: 1402: 1401: 1384:(9): 2435–2452. 1373: 1367: 1366: 1358: 1352: 1351: 1326:(9): 2416–2434. 1315: 1306: 1305: 1303: 1301: 1270: 1264: 1263: 1227: 1221: 1220: 1210: 1178: 1172: 1171: 1161: 1151: 1119: 1113: 1108: 1102: 1101: 1091: 1073: 1049: 1043: 1042: 1034: 1025: 1024: 1022: 1020: 1014: 1007: 996: 987: 986: 966: 957: 956: 954: 952: 946: 939: 928: 919: 918: 908: 898: 874: 868: 867: 866:on 27 July 2011. 865: 859:. Archived from 858: 849: 843: 842: 832: 800: 770:Autonomous agent 341:Invasive species 336:Invasive species 284:British Columbia 192: 185: 178: 18: 17: 3260: 3259: 3255: 3254: 3253: 3251: 3250: 3249: 3235: 3234: 3233: 3228: 3205: 3204: 3193: 3191: 3190: 3174: 3135:Coral bleaching 3121: 3102:Seagrass meadow 2999:Marine habitats 2989: 2963:Coral reef fish 2929: 2915:Marine protists 2891: 2829: 2820: 2791:Ocean turbidity 2766:Marine food web 2715: 2694: 2636: 2575:River ecosystem 2528:Freshwater fish 2501: 2492: 2298:Bioluminescence 2283:Aquatic science 2200: 2186: 2181: 2151: 2146: 2065: 2027: 1982: 1934: 1802: 1793: 1658: 1653: 1623: 1622: 1577: 1573: 1528: 1524: 1477: 1473: 1428: 1424: 1409: 1405: 1374: 1370: 1359: 1355: 1316: 1309: 1299: 1297: 1295: 1271: 1267: 1228: 1224: 1179: 1175: 1134:(2): e1005387. 1120: 1116: 1109: 1105: 1064:(3): e1009934. 1050: 1046: 1035: 1028: 1018: 1016: 1012: 1005: 997: 990: 967: 960: 950: 948: 944: 937: 929: 922: 875: 871: 863: 856: 850: 846: 821:10.1002/wsbm.45 801: 790: 785: 766: 692: 633: 613: 607: 496: 427:North Yorkshire 411: 338: 272: 263: 254: 245: 236: 228: 220: 218:Characteristics 196: 167: 124: 67: 12: 11: 5: 3258: 3248: 3247: 3230: 3229: 3227: 3226: 3216: 3202: 3187: 3184: 3183: 3180: 3179: 3176: 3175: 3173: 3172: 3167: 3162: 3157: 3152: 3147: 3142: 3137: 3131: 3129: 3123: 3122: 3120: 3119: 3114: 3109: 3104: 3099: 3094: 3089: 3084: 3079: 3074: 3069: 3064: 3059: 3054: 3049: 3044: 3039: 3034: 3029: 3024: 3019: 3014: 3009: 3003: 3001: 2995: 2994: 2991: 2990: 2988: 2987: 2982: 2981: 2980: 2975: 2970: 2965: 2960: 2953:Saltwater fish 2950: 2948:Marine reptile 2945: 2939: 2937: 2931: 2930: 2928: 2927: 2922: 2920:Marine viruses 2917: 2912: 2907: 2901: 2899: 2897:Microorganisms 2893: 2892: 2890: 2889: 2887:Wild fisheries 2884: 2879: 2874: 2869: 2864: 2859: 2854: 2849: 2843: 2837: 2831: 2830: 2823: 2821: 2819: 2818: 2813: 2808: 2803: 2801:Thorson's rule 2798: 2793: 2788: 2783: 2778: 2773: 2768: 2763: 2758: 2756:Marine biology 2753: 2748: 2743: 2738: 2733: 2727: 2725: 2717: 2716: 2704: 2703: 2700: 2699: 2696: 2695: 2693: 2692: 2691: 2690: 2685: 2680: 2675: 2667: 2662: 2661: 2660: 2655: 2644: 2642: 2638: 2637: 2635: 2634: 2633: 2632: 2627: 2622: 2617: 2615:Brackish marsh 2612: 2602: 2597: 2592: 2587: 2582: 2577: 2572: 2567: 2566: 2565: 2555: 2550: 2545: 2543:Lake ecosystem 2540: 2535: 2533:Hyporheic zone 2530: 2525: 2520: 2515: 2509: 2507: 2503: 2502: 2495: 2493: 2491: 2490: 2485: 2480: 2475: 2470: 2465: 2460: 2455: 2450: 2445: 2440: 2435: 2430: 2425: 2420: 2415: 2410: 2405: 2400: 2395: 2390: 2385: 2383:Microbial loop 2380: 2375: 2370: 2365: 2360: 2355: 2350: 2345: 2340: 2335: 2330: 2328:Eutrophication 2325: 2320: 2315: 2310: 2308:Cascade effect 2305: 2300: 2295: 2290: 2285: 2280: 2275: 2270: 2265: 2260: 2255: 2254: 2253: 2248: 2243: 2236:Aquatic animal 2233: 2228: 2223: 2218: 2212: 2210: 2202: 2201: 2188: 2187: 2180: 2179: 2172: 2165: 2157: 2148: 2147: 2145: 2144: 2139: 2134: 2129: 2124: 2122:Quorum sensing 2119: 2114: 2109: 2104: 2099: 2094: 2089: 2084: 2079: 2073: 2071: 2070:Related topics 2067: 2066: 2064: 2063: 2058: 2056:Swarm robotics 2053: 2048: 2043: 2037: 2035: 2033:Swarm robotics 2029: 2028: 2026: 2025: 2020: 2015: 2014: 2013: 2003: 1998: 1992: 1990: 1984: 1983: 1981: 1980: 1975: 1970: 1965: 1960: 1955: 1950: 1944: 1942: 1936: 1935: 1933: 1932: 1927: 1926: 1925: 1924: 1923: 1908: 1907: 1906: 1901: 1891: 1890: 1889: 1884: 1879: 1874: 1867:Fish migration 1864: 1862:Cell migration 1859: 1858: 1857: 1852: 1845:Bird migration 1842: 1841: 1840: 1838:coded wire tag 1835: 1834: 1833: 1823: 1812: 1810: 1804: 1803: 1796: 1794: 1792: 1791: 1786: 1781: 1776: 1775: 1774: 1764: 1763: 1762: 1757: 1747: 1746: 1745: 1735: 1734: 1733: 1731:feeding frenzy 1723: 1718: 1713: 1712: 1711: 1701: 1700: 1699: 1694: 1684: 1679: 1674: 1668: 1666: 1660: 1659: 1652: 1651: 1644: 1637: 1629: 1621: 1620: 1591:(3): 649–661. 1571: 1522: 1487:(2): 220–235. 1471: 1442:(5): 640–651. 1422: 1403: 1368: 1353: 1307: 1293: 1265: 1238:(1): 186–204. 1222: 1173: 1114: 1103: 1044: 1026: 988: 977:(4): 481–496. 958: 920: 869: 844: 815:(2): 159–171. 787: 786: 784: 781: 780: 779: 773: 765: 762: 691: 688: 632: 629: 621:cellular potts 609:Main article: 606: 603: 516:eutrophication 504:Aquatic plants 495: 492: 410: 407: 348:-specific and 337: 334: 297:lodgepole pine 271: 268: 262: 259: 253: 250: 244: 241: 235: 232: 227: 224: 219: 216: 198: 197: 195: 194: 187: 180: 172: 169: 168: 166: 165: 163:Swarm robotics 160: 155: 150: 145: 140: 134: 131: 130: 126: 125: 123: 122: 120:Software agent 117: 116: 115: 110: 105: 95: 90: 85: 79: 76: 75: 69: 68: 66: 65: 60: 55: 50: 44: 41: 40: 34: 33: 27: 26: 9: 6: 4: 3: 2: 3257: 3246: 3243: 3242: 3240: 3225: 3217: 3214: 3209: 3203: 3200: 3189: 3188: 3185: 3171: 3168: 3166: 3163: 3161: 3158: 3156: 3153: 3151: 3148: 3146: 3143: 3141: 3138: 3136: 3133: 3132: 3130: 3128: 3124: 3118: 3115: 3113: 3110: 3108: 3107:Sponge ground 3105: 3103: 3100: 3098: 3095: 3093: 3090: 3088: 3085: 3083: 3080: 3078: 3075: 3073: 3072:Marine biomes 3070: 3068: 3065: 3063: 3060: 3058: 3055: 3053: 3050: 3048: 3045: 3043: 3040: 3038: 3035: 3033: 3030: 3028: 3025: 3023: 3020: 3018: 3015: 3013: 3010: 3008: 3005: 3004: 3002: 3000: 2996: 2986: 2983: 2979: 2976: 2974: 2973:Demersal fish 2971: 2969: 2968:Deep-sea fish 2966: 2964: 2961: 2959: 2956: 2955: 2954: 2951: 2949: 2946: 2944: 2943:Marine mammal 2941: 2940: 2938: 2936: 2932: 2926: 2923: 2921: 2918: 2916: 2913: 2911: 2908: 2906: 2903: 2902: 2900: 2898: 2894: 2888: 2885: 2883: 2880: 2878: 2875: 2873: 2870: 2868: 2865: 2863: 2860: 2858: 2855: 2853: 2850: 2848: 2845: 2844: 2841: 2838: 2836: 2832: 2827: 2817: 2814: 2812: 2809: 2807: 2804: 2802: 2799: 2797: 2794: 2792: 2789: 2787: 2784: 2782: 2779: 2777: 2774: 2772: 2769: 2767: 2764: 2762: 2759: 2757: 2754: 2752: 2749: 2747: 2744: 2742: 2739: 2737: 2734: 2732: 2729: 2728: 2726: 2722: 2718: 2714: 2709: 2705: 2689: 2686: 2684: 2681: 2679: 2676: 2674: 2671: 2670: 2668: 2666: 2663: 2659: 2656: 2654: 2651: 2650: 2649: 2646: 2645: 2643: 2639: 2631: 2628: 2626: 2623: 2621: 2618: 2616: 2613: 2611: 2608: 2607: 2606: 2603: 2601: 2598: 2596: 2593: 2591: 2588: 2586: 2583: 2581: 2578: 2576: 2573: 2571: 2568: 2564: 2561: 2560: 2559: 2556: 2554: 2551: 2549: 2546: 2544: 2541: 2539: 2536: 2534: 2531: 2529: 2526: 2524: 2521: 2519: 2516: 2514: 2511: 2510: 2508: 2504: 2499: 2489: 2486: 2484: 2481: 2479: 2476: 2474: 2473:Trophic level 2471: 2469: 2466: 2464: 2461: 2459: 2456: 2454: 2451: 2449: 2446: 2444: 2441: 2439: 2438:Sediment trap 2436: 2434: 2431: 2429: 2426: 2424: 2421: 2419: 2416: 2414: 2413:Phytoplankton 2411: 2409: 2406: 2404: 2401: 2399: 2396: 2394: 2391: 2389: 2386: 2384: 2381: 2379: 2376: 2374: 2371: 2369: 2366: 2364: 2361: 2359: 2356: 2354: 2351: 2349: 2346: 2344: 2341: 2339: 2336: 2334: 2331: 2329: 2326: 2324: 2321: 2319: 2316: 2314: 2311: 2309: 2306: 2304: 2301: 2299: 2296: 2294: 2291: 2289: 2286: 2284: 2281: 2279: 2276: 2274: 2271: 2269: 2266: 2264: 2263:Aquatic plant 2261: 2259: 2256: 2252: 2249: 2247: 2244: 2242: 2239: 2238: 2237: 2234: 2232: 2229: 2227: 2226:Anoxic waters 2224: 2222: 2219: 2217: 2214: 2213: 2211: 2207: 2203: 2199: 2193: 2189: 2185: 2178: 2173: 2171: 2166: 2164: 2159: 2158: 2155: 2143: 2140: 2138: 2135: 2133: 2130: 2128: 2125: 2123: 2120: 2118: 2115: 2113: 2110: 2108: 2105: 2103: 2100: 2098: 2095: 2093: 2090: 2088: 2085: 2083: 2080: 2078: 2075: 2074: 2072: 2068: 2062: 2059: 2057: 2054: 2052: 2049: 2047: 2044: 2042: 2039: 2038: 2036: 2034: 2030: 2024: 2021: 2019: 2016: 2012: 2009: 2008: 2007: 2004: 2002: 1999: 1997: 1996:Active matter 1994: 1993: 1991: 1989: 1985: 1979: 1976: 1974: 1971: 1969: 1966: 1964: 1961: 1959: 1956: 1954: 1951: 1949: 1946: 1945: 1943: 1941: 1937: 1931: 1928: 1922: 1919: 1918: 1917: 1914: 1913: 1912: 1909: 1905: 1902: 1900: 1897: 1896: 1895: 1892: 1888: 1885: 1883: 1880: 1878: 1875: 1873: 1872:diel vertical 1870: 1869: 1868: 1865: 1863: 1860: 1856: 1853: 1851: 1848: 1847: 1846: 1843: 1839: 1836: 1832: 1829: 1828: 1827: 1824: 1822: 1819: 1818: 1817: 1814: 1813: 1811: 1809: 1805: 1800: 1790: 1787: 1785: 1782: 1780: 1777: 1773: 1770: 1769: 1768: 1765: 1761: 1758: 1756: 1753: 1752: 1751: 1748: 1744: 1741: 1740: 1739: 1736: 1732: 1729: 1728: 1727: 1724: 1722: 1719: 1717: 1714: 1710: 1709:herd behavior 1707: 1706: 1705: 1702: 1698: 1695: 1693: 1690: 1689: 1688: 1685: 1683: 1680: 1678: 1675: 1673: 1670: 1669: 1667: 1665: 1661: 1657: 1650: 1645: 1643: 1638: 1636: 1631: 1630: 1627: 1616: 1612: 1608: 1604: 1599: 1594: 1590: 1586: 1582: 1575: 1567: 1563: 1558: 1553: 1549: 1545: 1541: 1537: 1533: 1526: 1518: 1514: 1510: 1506: 1502: 1498: 1494: 1490: 1486: 1482: 1475: 1467: 1463: 1458: 1453: 1449: 1445: 1441: 1437: 1433: 1426: 1418: 1414: 1407: 1399: 1395: 1391: 1387: 1383: 1379: 1372: 1364: 1357: 1349: 1345: 1341: 1337: 1333: 1329: 1325: 1321: 1314: 1312: 1296: 1290: 1286: 1282: 1278: 1277: 1269: 1261: 1257: 1253: 1249: 1245: 1241: 1237: 1233: 1226: 1218: 1214: 1209: 1204: 1200: 1196: 1192: 1188: 1184: 1177: 1169: 1165: 1160: 1155: 1150: 1145: 1141: 1137: 1133: 1129: 1125: 1118: 1112: 1107: 1099: 1095: 1090: 1085: 1081: 1077: 1072: 1067: 1063: 1059: 1055: 1048: 1040: 1033: 1031: 1011: 1004: 1003: 995: 993: 984: 980: 976: 972: 965: 963: 943: 936: 935: 927: 925: 916: 912: 907: 902: 897: 892: 888: 884: 880: 873: 862: 855: 848: 840: 836: 831: 826: 822: 818: 814: 810: 806: 799: 797: 795: 793: 788: 777: 774: 771: 768: 767: 761: 758: 754: 749: 747: 743: 737: 735: 730: 726: 722: 716: 714: 710: 706: 702: 697: 687: 685: 681: 677: 672: 666: 664: 659: 654: 650: 646: 642: 638: 628: 626: 622: 618: 612: 602: 599: 594: 590: 586: 581: 579: 575: 571: 567: 563: 558: 554: 550: 546: 542: 538: 534: 530: 529: 524: 521: 517: 513: 509: 505: 501: 491: 487: 485: 480: 476: 472: 468: 464: 459: 455: 450: 448: 444: 440: 436: 432: 428: 424: 420: 416: 406: 402: 400: 395: 392: 391:heterogeneous 388: 384: 380: 376: 372: 368: 364: 358: 355: 351: 347: 342: 333: 331: 327: 323: 318: 315: 310: 305: 300: 298: 293: 289: 285: 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Index

a series
Multi-agent systems
Multi-agent simulation
Agent-based computational economics
Agent-based model in biology
Agent-based social simulation
Agent-based modeling software
Agent-oriented programming
Auto-GPT
Botnets
FIPA
Platforms for software agents
JADE
JACK
GORITE
Software agent
Distributed artificial intelligence
Multi-agent pathfinding
Multi-agent planning
Multi-agent reinforcement learning
Self-propelled particles
Swarm robotics
v
t
e
Agent-based models
biology
computational modeling
mountain pine beetle
British Columbia

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