A Self-Replication Basis for Designing Complex Agents
This work addresses the challenge of agent design in AI, but it appears incremental as it builds on existing evolutionary methods without clear broad impact.
The paper tackles the problem of designing complex artificial agents by proposing a self-replication-based mechanism, demonstrating its validity through simulation on standard evolutionary computation problems.
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.