The Ecosystem Path to AGI
Paper i proceeding, 2022

We start by discussing the link between ecosystem simulators and artificial general intelligence (AGI). 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.



Reinforcement learning

Neural networks



Claes Strannegård

Chalmers, Data- och informationsteknik, Data Science och AI

Niklas Engsner

Chalmers, Data- och informationsteknik, Data Science

Pietro Ferrari

Student vid Chalmers

Hans Glimmerfors

Student vid Chalmers

Marcus Hilding Södergren

Student vid Chalmers

Tobias Karlsson

Chalmers, Data- och informationsteknik, Data Science och AI

Birger Kleve

Student vid Chalmers

Victor Skoglund

Student vid Chalmers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13154 LNAI 269-278
9783030937577 (ISBN)

14th International Conference on Artificial General Intelligence, AGI 2021
San Francisco, USA,




Datavetenskap (datalogi)



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