Cooperative control of environmental extremes by artificial intelligent agents

TL;DR

This paper demonstrates how artificial agents can collaboratively manage ecosystems by balancing resource harvesting and fire suppression, leading to sustainable biomass levels and reduced extreme fire events.

Contribution

It introduces a novel AI-based framework for ecological engineering that achieves adaptive management of fire-prone landscapes through cooperative agent strategies.

Findings

Agents develop strategies that balance resource harvesting and fire suppression.

The system evolves to favor high biomass and fire control.

Ecological engineering strategies emerge naturally from agent interactions.

Abstract

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential A.I. management of complex ecosystems are discussed.