AI Tool for Exploring How Economic Activities Impact Local Ecosystems

TL;DR

This paper introduces an AI ecosystem simulator that models how economic activities influence local ecosystems using 3D terrain and animal models trained with deep reinforcement learning within a game engine environment.

Contribution

It presents a novel AI-based ecosystem simulation framework combining geographic data, 3D animal models, and reinforcement learning to study economic impacts on biodiversity.

Findings

Simulates ecosystem development with economic activities

Models biodiversity effects of land cover change

Demonstrates potential for environmental impact analysis

Abstract

We present an AI-based ecosystem simulator that uses three-dimensional models of the terrain and animal models controlled by deep reinforcement learning. The simulations take place in a game engine environment, which enables continuous visual observation of the ecosystem model. The terrain models are generated from geographic data with altitudes and land cover type. The animal models combine three-dimensional conformation models with animation schemes and decision-making mechanisms trained with deep reinforcement learning in increasingly complex environments (curriculum learning). We show how AI tools of this kind can be used for modeling the development of specific ecosystems with and without different forms of economic activities. In particular, we show how they might be used for modeling local biodiversity effects of land cover change, exploitation of natural resources, pollution, invasive species, and climate change.