Evolution and learning in artificial ecosystems
Paper in proceedings, 2018

A generic model is presented for ecosystems inhabited
by artificial animals, or animats, that
develop over time. The individual animats develop
continuously by means of generic mechanisms
for learning, forgetting, and decisionmaking.
At the same time, the animat populations
develop in an evolutionary process based
on fixed mechanisms for sexual and asexual
reproduction, mutation, and death. The animats
of the ecosystems move, eat, learn, make
decisions, interact with other animats, reproduce,
and die. Each animat has its individual
sets of homeostatic variables, sensors, and motors.
It also has its own memory graph that
forms the basis of its decision-making. This
memory graph has an architecture (i.e. graph
topology) that changes over time via mechanisms
for adding and removing nodes. Our approach
combines genetic algorithms, reinforcement
learning, homeostatic decision-making,
and dynamic concept formation. To illustrate
the generality of the model, five examples of
ecosystems are given, ranging from a simple
world inhabited by a single frog to a more complex
world in which grass, sheep, and wolves
interact.

Author

Claes Strannegård

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

Wen Xu

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

Niklas Engsner

John A. Endler

Deakin University

In Proceedings of IJCAI-18 Workshop on Architectures for Generality and Autonomy, 2018

IJCAI-18 Workshop on Architectures for Generality and Autonomy, 2018
Stockholm, Sweden,

Subject Categories

Computer and Information Science

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1/15/2019