Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning

Brearcliffe, Dale; Crooks, Andrew

doi:10.1007/978-3-030-83418-0_3

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…Frontiers in Systems Biology frontiersin.org 22 Figure 8. Second, the specific type of ML algorithm used with an ABM strongly impacts the emergent results and veracity of predictions by the ABM, making it critical to evaluate multiple ML/ABM integration schemes and select the algorithmic approach with highest accuracy and/or similarity to the decision-making structure of the (actual) modeled system; notably, a similar point was recently made in the social sciences by Brearcliffe and Crooks (2021), who found that "different ML methods used in the same [ABM] model impact the simulation outcome". Just as there is no universally "best" approach to ABM or ML individually, there is no one-size-fits-all solution for their integration: systematic explorations of ML/ABM integration schemes would seem to be a worthwhile endeavor.…”

Section: Discussionmentioning

confidence: 99%

“…Several prior reviews have described how ML can be leveraged in computational modeling, e.g., by Alber et al (2019) and by Peng et al (2021). In addition, the idea of synergistically integrating ML and ABM (Figure 2) has existed since at least Rand's (2006) early report, and includes more recent works such as by Giabbanelli (2019), Brearcliffe and Crooks (2021), and Zhang et al (2021). The remainder of our present Review focuses more on the utility of ML within ABMs, and attempts to offer some guiding principles on how and when these integrations are feasible in simulating different scales of biology.…”

Section: • Parameter Calibration and Surrogate Models Of Abmsmentioning

confidence: 99%

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Innovations in integrating machine learning and agent-based modeling of biomedical systems

Sivakumar

Mura²,

Peirce³

2022

Front. Syst. Biol.

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Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.

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Section: Discussionmentioning

confidence: 99%

Section: • Parameter Calibration and Surrogate Models Of Abmsmentioning

confidence: 99%

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Sivakumar

Mura²,

Peirce³

2022

Front. Syst. Biol.

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“… Brearcliffe and Crooks (2021) include ML techniques (Evolutionary Computing, Q Learning, and State-Action-Reward-State-Action) into the ABM called Sugarscape , an artificial world game of 51 × 51 cell grid containing a renewable resource (i.e., sugar) for which agents can capture and metabolize. As a result, they can pollute, die, reproduce, inherit sources, transfer information, trade or borrow sugar, generate immunity, or transmit diseases ( Epstein and Axtell, 1996 ).…”

Section: Hybrid Intelligent Mechanistic Modelsmentioning

confidence: 99%

“…As a result, they can pollute, die, reproduce, inherit sources, transfer information, trade or borrow sugar, generate immunity, or transmit diseases ( Epstein and Axtell, 1996 ). Simulations of the intelligent ABM model by Brearcliffe and Crooks (2021) ( https://tinyurl.com/ML-Agents ) suggested that ML methods can be integrated into ABM, but ML may not always yield the best results. Animal scientists are yet to adopt ABM to understand the grazing behavior of herbivores and how climate change can alter it, and iABM might be a more suitable approach.…”

Section: Hybrid Intelligent Mechanistic Modelsmentioning

confidence: 99%

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

Tedeschi

2022

Journal of Animal Science

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A renewed interest in data analytics and decision support systems in developing automated computer systems is facilitating the emergence of hybrid intelligent systems by combining artificial intelligence (AI) algorithms with classical modeling paradigms such as mechanistic modeling (HIMM) and agent-based models (iABM). Data analytics have evolved remarkably, and the scientific community may not yet fully grasp the power and limitations of some tools. Existing statistical assumptions might need to be re-assessed to provide a more thorough competitive advantage in animal production systems towards sustainability. This paper discussed the evolution of data analytics from a competitive advantage perspective within academia and illustrated the combination of different advanced technological systems in developing HIMM. The progress of analytical tools was divided into three stages: collect and respond, predict and prescribe, and smart learning and policy-making, depending on the level of their sophistication (simple to complicated analysis). The collect and respond stage is responsible for ensuring the data is correct and free of influential data points, and it represents the data and information phases for which data is cataloged and organized. The predict and prescribe stage results in gained knowledge from the data and comprises most predictive modeling paradigms, and optimization and risk assessment tools are used to prescribe future decision-making opportunities. The third stage aims to apply the information obtained in the previous stages to foment knowledge and use it for rational decisions. This stage represents the pinnacle of acquired knowledge that leads to wisdom, and AI technology is intrinsic. Although still incipient, HIMM and iABM form the forthcoming stage of competitive advantage. HIMM may not increase our ability to understand the underlying mechanisms controlling the outcomes of a system, but it may increase the predictive ability of existing models by helping the analyst explain more of the data variation. The scientific community still has some issues to be resolved, including the lack of transparency and reporting of AI that might limit code reproducibility. It might be prudent for the scientific community to avoid the shiny object syndrome (i.e., AI) and look beyond the current knowledge to understand the mechanisms that might improve productivity and efficiency to lead agriculture towards sustainable and responsible achievements.

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“…The Magellan navigator is activated and the settings are adjusted based on the coordinate zone where the navigator is located. Adjustments to the navigator settings are made in the following sequence [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Application of GIS technologies in the methods of geodetic work in the mapping of demographic processes

Abdurakhmonov,

Berdiyev,

Gulov

et al. 2023

E3S Web Conf.

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Analytical work on rapid data identification based on modeling spatial analysis of data based on geographic information systems (GIS) and technologies is required. As a result, with the help of GIS technologies, the population density in the regions; natural population growth dynamics; a histogram with respect to species; the dependence of the location of the population on the terrain; it is possible to carry out analytical work through spatial analysis of data describing demographic processes such as employment. The study focuses on the ways of geolocation of data from the GPS navigator and the software “Geodesist” in the automatic remote transmission of various demographic processes in the field, including population-related information online to the GIS technology database.

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Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning

Cited by 5 publications

References 38 publications

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Innovations in integrating machine learning and agent-based modeling of biomedical systems

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

Application of GIS technologies in the methods of geodetic work in the mapping of demographic processes

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