The Ecosystem Path to General AI
Preprint, 2021

We start by discussing the link between ecosystem simulators and general AI. Then we present the open-source ecosystem simulator Ecotwin, which is based on the game engine Unity and operates on ecosystems containing inanimate objects like mountains and lakes, as well as organisms such as animals and plants. Animal cognition is modeled by integrating three separate networks: (i) a reflex network for hard-wired reflexes; (ii) a happiness network that maps sensory data such as oxygen, water, energy, and smells, to a scalar happiness value; and (iii) a policy network for selecting actions. The policy network is trained with reinforcement learning (RL), where the reward signal is defined as the happiness difference from one time step to the next. All organisms are capable of either sexual or asexual reproduction, and they die if they run out of critical resources. We report results from three studies with Ecotwin, in which natural phenomena emerge in the models without being hardwired. First, we study a terrestrial ecosystem with wolves, deer, and grass, in which a Lotka-Volterra style population dynamics emerges.
Second, we study a marine ecosystem with phytoplankton, copepods, and krill, in which a diel vertical migration behavior emerges.
Third, we study an ecosystem involving lethal dangers, in which certain agents that combine RL with reflexes outperform pure RL agents.

ecosystem · neural networks · happiness · reflexes · reinforcement learning


Claes Strannegård

Data Science and AI

Niklas Engsner

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Pietro Ferrari

Student at Chalmers

Hans Glimmerfors

Student at Chalmers

Marcus Hilding Södergren

Student at Chalmers

Tobias Karlsson

Data Science and AI 1

Birger Kleve

Student at Chalmers

Victor Skoglund

Student at Chalmers

Areas of Advance

Information and Communication Technology

Driving Forces

Sustainable development

Subject Categories


Computer Science

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Latest update

9/5/2022 8