A Self-Replication Basis for Designing Complex Agents

TL;DR

This paper introduces a self-replication mechanism for designing complex agents, demonstrating its effectiveness in evolutionary simulation and discussing its advantages and challenges compared to traditional methods.

Contribution

It presents a novel self-replication-based approach for creating complex artificial agents, validated through simulation on standard evolutionary problems.

Findings

Successful simulation of simple evolutionary problems

Fundamental differences from traditional agent design methods

Potential advantages and future challenges identified

Abstract

In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity. We demonstrate the validity of this approach by solving simple, standard evolutionary computation problems in simulation. In the context of these simulation results, we describe the fundamental differences of this approach when compared to traditional approaches. Further, we highlight the possible advantages of applying this approach to the problem of designing complex artificial agents, along with the potential drawbacks and issues to be addressed in the future.