A Theory of Foundational Meaning Generation in Autonomous Systems, Natural and Artificial

The concept of ‘meaning’ has long been a subject of philosophy and people use the term regularly. Theories of meaning detailed enough to serve as blueprints in the design of intelligent artificial systems have however been few. Here we present a theory of foundational meaning creation – the phenomenon proper – sufficiently broad to apply to natural agents yet concrete enough to be implemented in a running artificial system. The theory states that meaning generation is a process bound in the present now, resting on the concept of reliable causal models. By unifying goals, predictions, plans, situations and knowledge, it explains how ampliative reasoning and explicit representations of causal relations participate in the meaning generation process. According to the theory, meaning and autonomy are two sides of the same coin: Meaning generation without autonomy is meaningless; autonomy without meaning is impossible.