Combining Evolution and Learning in Computational Ecosystems

Abstract

Although animals such as spiders, fish, and birds have very different anatomies, the basic mechanisms that govern their perception, decision-making, learning, reproduction, and death have striking similarities. These mechanisms have apparently allowed the development of general intelligence in nature. This led us to the idea of approaching artificial general intelligence (AGI) by constructing a generic artificial animal (animat) with a configurable body and fixed mechanisms of perception, decision-making, learning, reproduction, and death. One instance of this generic animat could be an artificial spider, another an artificial fish, and a third an artificial bird. The goal of all decision-making in this model is to maintain homeostasis. Thus actions are selected that might promote survival and reproduction to varying degrees. All decision-making is based on knowledge that is stored in network structures. Each animat has two such network structures: a genotype and a phenotype. The genotype models the initial nervous system that is encoded in the genome (“the brain at birth”), while the phenotype represents the nervous system in its present form (“the brain at present”). Initially the phenotype and the genotype coincide, but then the phenotype keeps developing as a result of learning, while the genotype essentially remains unchanged. The model is extended to ecosystems populated by animats that develop continuously according to fixed mechanisms for sexual or asexual reproduction, and death. Several examples of simple ecosystems are given. We show that our generic animat model possesses general intelligence in a primitive form. In fact, it can learn simple forms of locomotion, navigation, foraging, language, and arithmetic.

Investigating Defensive Colouration in Nudibranch Molluscs using a Novel Analytical Framework for the Study of Animal Colour Patterns​​​​​​​

“…An interesting use of AI will be the exploration of its predictive capabilities in combination with our ever-increasing ability to obtain large amounts of data in combination with agent based models predicting adaptive outcomes of animal interactions with other animals in the context of a given visual environment (e.g. Strannegård, Xu, Engsner, Endler, & Marshall, 2020;Talas et al, 2020). Such approaches are of great interest regarding the need of tools and methods with predictive abilities i.e.…”