City Research Online

Abstract

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 representations, which rely exclusively on raw sensory data, biological representations incorporate relational and associative information that embed a rich and structured concept space. 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 and complementary components work in parallel to detect and recover relevant concepts through a matching process and create new ones, respectively, in a process akin to cognitive Reflective Reasoning and Blending. If Reflective Reasoning fails to offer a suitable solution, a blending operation creates 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.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This version of the contribution has been accepted for publication, after peer review but is not the Version of record and does not reflect post-acceptance improvements, or any corrections. The Version of Record will be available online at: https://www.springer.com/series/1244 Use of the Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
Publisher Keywords:	Affordances, Generalisation, Creativity, Representational Learning, Reinforcement Learning, Learning Transfer
Subjects:	H Social Sciences > HM Sociology H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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