  Traditionally, the way one evaluates the performance of an Artificial
Intelligence (AI) system is via a comparison to human performance in specific
tasks, treating humans as a reference for high-level cognition. However, these
comparisons leave out important features of human intelligence: the capability
to transfer knowledge and make complex decisions based on emotional and
rational reasoning. These decisions are influenced by current inferences as
well as prior experiences, making the decision process strongly subjective and
apparently biased. In this context, a definition of compositional intelligence
is necessary to incorporate these features in future AI tests. Here, a concrete
implementation of this will be suggested, using recent developments in quantum
cognition, natural language and compositional meaning of sentences, thanks to
categorical compositional models of meaning.
