Modern approaches to modeling adaptive behavior of agent in virtual ecosystems

DOI: 10.31673/2412-9070.2025.050123

Abstract

Modeling adaptive behavior in virtual ecosystems is a promising and interdisciplinary research area that combines the achievements of computer science, ecology, sociology, and artificial intelligence. This article reviews modern methods and tools that allow creating multi-agent systems (MAS) that are able to adapt to environmental changes in virtual space.
Particular attention is paid to key technologies such as neural networks, evolutionary algorithms, and physically based simulations. The advantages and limitations of popular platforms, including AnyLogic, Unity, TensorFlow, and ML.NET, are also analyzed.
Agent-based modeling (ABM) is a basic tool in creating autonomous agents that are able to respond to environmental changes. The capabilities of such platforms as NetLogo and AnyLogic are compared: the former is convenient for building simple models, while the latter allows for implementting more complex scenarios, but requires deeper technical knowledge and more computational resources.
Neural networks and machine learning (ML) methods play a key role in the development of adaptive behavior of agents. TensorFlow shows high efficiency when working with large amounts of data, and PyTorch is distinguished by its flexibility and convenience for rapid prototyping, which is especially important in the initial stages of research.
Evolutionary algorithms and genetic programming have proven themselves well in adaptation and optimization tasks. Libraries such as DEAP (Python) and GALib (C++) allow you to model the mechanisms of natural selection, although they require careful parameter tuning and significant computing power.
Multi-agent systems (MAS) are considered as an extension of the ABM approach, with an emphasis on the interaction of agents with each other. The Repast and MASON platforms allow you to model complex collective dynamics - both in biological and social systems. The integration of physical simulations in Unity ML-Agents or Unreal Engine allows you to create more realistic scenarios of agent interaction with the environment. Unity is distinguished by its broad support for ML tools, while Unreal Engine provides extremely high-quality visualization.
The application of adaptive behavior modeling covers a wide range of industries: from ecology (modeling interactions between species) to economics (analysis of consumer behavior) and sociology (study of information dissemination in networks). This once again confirms the universality of app roaches to creating virtual ecosystems.
At the same time, certain challenges remain: significant computational costs, the complexity of achieving plausible agent behavior, as well as the need for close interdisciplinary cooperation. In the future, active implementation of the latest technologies is expected - in particular, quantum computing, real-time data integration via IoT, and combining different approaches to increase the accuracy of simulations.
As a result, adaptive behavior modeling opens up new horizons in the analysis of complex systems. For simple models, NetLogo is sufficient, and for more complex and more realistic simulations, TensorFlow, Unity, or AnyLogic are better suited. The prospects of this direction are associated with hybrid solutions that combine the advantages of neural networks, agent-based approaches, and evolutionary algorithms, creating large-scale and reliable virtual ecosystems.

Keywords: adaptive behavior; multi-agent systems; machine learning; neural networks; virtual ecosystems; evolutionary algorithms; TensorFlow; Unity ML-Agents; AnyLogic.