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

Författare

Claes Strannegård

Data Science och AI

Styrkeområden

Informations- och kommunikationsteknik

Drivkrafter

Hållbar utveckling

Ämneskategorier

Ekologi

Datavetenskap (datalogi)

Mer information

Senast uppdaterat

2021-08-21