Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems

TL;DR

This paper proposes an information-theoretic framework for representing agents in dynamical systems using integrated spatiotemporal patterns, aiming to address limitations of existing methods in artificial life applications.

Contribution

It introduces a novel notion of integrated spatiotemporal patterns as a foundational agent representation in dynamical systems, grounded in information theory.

Findings

Patterns can capture metabolism, motility, and counterfactual variation.

Preliminary experiments support the effectiveness of the proposed patterns.

The approach addresses key limitations of existing agent representations.

Abstract

We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life. Using a thought experiment involving a fictitious dynamical systems model of the biosphere we argue that the metabolism, motility, and the concept of counterfactual variation should be compatible with any agent representation in dynamical systems. We then propose an information-theoretic notion of \emph{integrated spatiotemporal patterns} which we believe can serve as the basic building block of an agent definition. We argue that these patterns are capable of solving the problems mentioned before. We also test this in some preliminary experiments.