AI Tool for Exploring How Economic Activities Impact Local Ecosystems
Paper i proceeding, 2024

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.

3D terrain model

Ecosystem simulator

Deep reinforcement learning

Economic activities

Agent-based model


Claes Strannegård

Göteborgs universitet

Niklas Engsner

Chalmers, Data- och informationsteknik, Data Science

Rasmus Lindgren

Student vid Chalmers

Simon Olsson

Chalmers, Data- och informationsteknik, Data Science och AI

John Endler

Deakin University

Lecture Notes in Networks and Systems

23673370 (ISSN) 23673389 (eISSN)

Vol. 825 690-709
9783031477171 (ISBN)

Intelligent Systems Conference, IntelliSys 2023
Amsterdam, Netherlands,





Mer information

Senast uppdaterat