Bridging Generative Networks with the Common Model of Cognition
This work addresses a theoretical gap for researchers in AI and cognitive science, but it appears incremental as it builds on existing models without clear empirical validation.
The paper tackles the problem of integrating large generative network models with cognitive architectures by proposing a theoretical framework that adapts the Common Model of Cognition, restructuring it with shadow production systems to enable seamless connection and higher-level reasoning.
This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production system, which handles higher-level reasoning based on the shadow productions' output. Implementing this novel structure within the Common Model allows for a seamless connection between cognitive architectures and generative neural networks.