Information theoretic approach to interactive learning

TL;DR

This paper introduces an information theoretic framework for interactive learning that balances predictive power and model complexity, leading to optimal policies that naturally incorporate exploration and control without randomness.

Contribution

It proposes a unified theoretical approach integrating feedback, model selection, and decision-making based on maximizing predictive power with minimal complexity.

Findings

Optimal models capture causal structure at various abstraction levels.

Learner's policies inherently balance exploration and control.

Deterministic policies can be explorative due to feedback-driven optimization.

Abstract

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.