Abstract

This paper describes an attempt to repurpose Kant’s a priori psychology as the architectural blueprint for a machine learning system. First, it describes the conditions that must be satisfied for the agent to achieve unity of experience: the intuitions must be connected, via binary relations, so as to satisfy various unity conditions. Second, it shows how the categories are derived within this model: the categories are pure unary predicates that are derived from the pure binary relations. Third, I describe how Kant’s cognitive architecture has been implemented in a computer system (the Apperception Engine) and show in detail what it is like for the system to construct a unified experience from a sequence of raw sensory input.