Learning in LIDA

LIDA is a systems-level, biologically-inspired cognitive architecture. More than a decade of research on LIDA has seen much conceptual work on its learning mechanisms, and resulted in a set of conceptual commitments that constrain those mechanisms; perhaps the most essential of these constraints is the Conscious Learning Hypothesis from Global Workspace Theory, which asserts that all significant learning requires consciousness. Despite these successes, many conceptual challenges remain, and bridging the divide between LIDA’s conceptual model and its implementations has been challenging.The contributions of this paper are threefold: We present a detailed survey of learning in LIDA, during which we clarify, elaborate on, and synthesize together ideas from numerous papers, using updated terminology that reflects the continuing evolution of LIDA. We explore foundational issues in learning, such as, “What must be innate or built-in?” versus “What can be learned?”, the nature of LIDA’s representations, and the relationship between the LIDA conceptual model and its computational realizations. Finally, we provide a roadmap for future work. We believe that this paper will direct and catalyze our research endeavors, and provide a thorough introduction to the conceptual foundations of LIDA’s learning mechanisms that will be useful to anyone that would like a deeper understanding of LIDA or for those that plan to implement LIDA-based agents.