An Information Theoretic Representation of Agent Dynamics as Set Intersections

TL;DR

This paper introduces a set-based, information-theoretic framework for modeling agent interactions, demonstrating how agent dynamics can be represented as string intersections and analyzing their complexity within the context of universal AI.

Contribution

It proposes a novel set-theoretic and algorithmic information theory approach to represent and analyze agent interactions, linking it to universal AI models like AIXI.

Findings

Agent interactions modeled as string intersections.

Complexity properties of interactions analyzed.

Compatibility with universal AI demonstrated.

Abstract

We represent agents as sets of strings. Each string encodes a potential interaction with another agent or environment. We represent the total set of dynamics between two agents as the intersection of their respective strings, we prove complexity properties of player interactions using Algorithmic Information Theory. We show how the proposed construction is compatible with Universal Artificial Intelligence, in that the AIXI model can be seen as universal with respect to interaction.