Controllable Information Production

2026-01-30

AI Summary

This work addresses the limitations of existing information-theoretic intrinsic motivation methods, which rely on handcrafted random variables and lack autonomy. The authors propose a novel intrinsic motivation principle termed Controllable Information Production (CIP), derived from optimal control theory, that drives agents to autonomously explore and regulate chaotic dynamics by maximizing the difference in Kolmogorov–Sinai entropy between open-loop and closed-loop systems—without external rewards or human intervention. CIP formalizes the controllability of information production as an intrinsic drive, thereby eliminating dependence on extrinsic utilities or predefined variables and establishing a theoretical bridge between intrinsic and extrinsic behaviors. Empirical evaluations on standard benchmark tasks validate the efficacy of CIP and confirm its key theoretical properties.

Technology Category

• Machine Learning: Information Theory
• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Intrinsic Motivation (IM) is a paradigm for generating intelligent behavior without external utilities. The existing information-theoretic methods for IM are predominantly based on information transmission, which explicitly depends on the designer's choice of which random variables engage in transmission. In this work, we introduce a novel IM principle, Controllable Information Production (CIP), that avoids both external utilities and designer-specified variables. We derive the CIP objective from Optimal Control, showing a connection between extrinsic and intrinsic behaviors. CIP appears as the gap between open-loop and closed-loop Kolmogorov-Sinai entropies, which simultaneously rewards the pursuit and regulation of chaos. We establish key theoretical properties of CIP and demonstrate its effectiveness on standard IM benchmarks.

Problem

• Intrinsic Motivation
• Information Production
• Controllable Information
• Kolmogorov-Sinai Entropy
• Optimal Control

Innovation

• Controllable Information Production
• Intrinsic Motivation
• Optimal Control
• Kolmogorov-Sinai Entropy
• Information Theory