Towards a Framework for Observing Artificial Evolutionary Systems

TL;DR

This paper proposes an abstract formal framework for observing and analyzing evolutionary behavior in artificial life models, using high-level observations during simulations to identify life-like evolution.

Contribution

It introduces a novel formal framework based on high-level observations to systematically identify evolutionary processes in ALife models, illustrated with cellular automata and lambda calculus examples.

Findings

Framework successfully characterizes evolutionary behavior in ALife models

Design suggestions for ALife research based on the framework

Illustrative case studies demonstrate the framework's applicability

Abstract

Establishing the emergence of evolutionary behavior as a defining characteristic of 'life' is a major step in the Artificial life (ALife) studies. We present here an abstract formal framework for this aim based upon the notion of high-level observations made on the ALife model at hand during its simulations. An observation process is defined as a computable transformation from the underlying dynamic structure of the model universe to a tuple consisting of abstract components needed to establish the evolutionary processes in the model. Starting with defining entities and their evolutionary relationships observed during the simulations of the model, the framework prescribes a series of definitions, followed by the axioms (conditions) that must be met in order to establish the level of evolutionary behavior in the model. The examples of Cellular Automata based Langton Loops and Lambda calculus based Algorithmic Chemistry are used to illustrate the framework. Generic design suggestions for the ALife research are also drawn based upon the framework design and case study analysis.