  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.
