  Inspired by cognitive theories of creativity, this paper introduces a
computational model (AIGenC) that lays down the necessary components to enable
artificial agents to learn, use and generate transferable representations.
Unlike machine representation learning, which relies exclusively on raw sensory
data, biological representations incorporate relational and associative
information that embeds rich and structured concept spaces. The AIGenC model
poses a hierarchical graph architecture with various levels and types of
representations procured by different components. The first component, Concept
Processing, extracts objects and affordances from sensory input and encodes
them into a concept space. The resulting representations are stored in a dual
memory system and enriched with goal-directed and temporal information acquired
through reinforcement learning, creating a higher-level of abstraction. Two
additional components work in parallel to detect and recover relevant concepts
and create new ones, respectively, in a process akin to cognitive Reflective
Reasoning and Blending. The Reflective Reasoning unit detects and recovers from
memory concepts relevant to the task by means of a matching process that
calculates a similarity value between the current state and memory graph
structures. Once the matching interaction ends, rewards and temporal
information are added to the graph, building further abstractions. If the
reflective reasoning processing fails to offer a suitable solution, a blending
operation comes into place, creating new concepts by combining past
information. We discuss the model's capability to yield better
out-of-distribution generalisation in artificial agents, thus advancing toward
Artificial General Intelligence.
