arXiv:2403.18827v1 Announce Type: new
Abstract: 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.
Adapting the Common Model of Cognition for Large Generative Network Models: A Theoretical Framework
Introduction
In the field of artificial intelligence, cognitive architectures play a crucial role in understanding and simulating human-like intelligence. One widely used cognitive architecture is the Common Model of Cognition (CMC), which provides a framework for representing and organizing various cognitive processes. However, as the field progresses and more advanced generative neural network models emerge, there is a need to adapt the CMC to seamlessly integrate with these models. This article presents a theoretical framework that achieves this integration by restructuring the CMC using shadow production systems.
The Common Model of Cognition
Before delving into the proposed framework, it is essential to understand the basics of the Common Model of Cognition. The CMC is a modular architecture that consists of several interconnected modules representing different cognitive processes such as attention, perception, memory, language, and reasoning. These modules interact with each other, allowing for a comprehensive representation of human cognition.
The Need for Integration
Generative neural network models, such as deep learning architectures, have shown remarkable success in various tasks, including image and speech recognition, natural language processing, and even creative tasks like music and art generation. However, these models often lack a higher-level reasoning component that is critical for human-like intelligence.
By adapting the CMC to integrate with generative neural network models, we can create a hybrid architecture that combines the strengths of both approaches. This integration enables the neural network models to handle lower-level perceptual and pattern generation tasks, while the CMC’s central production system utilizes the output from these models for higher-level reasoning and decision-making.
Shadow Production Systems
The key concept in this framework is the introduction of shadow production systems. These systems act as peripheral modules connected to the central production system of the CMC. The shadow production systems receive input from the generative neural network models and generate shadow productions based on their output.
Shadow productions are similar to the rule-based productions used in traditional cognitive architectures. They represent knowledge in the form of condition-action rules that govern behavior. By structuring the CMC with shadow production systems, we establish a seamless connection between the generative neural network models and the higher-level cognitive processes.
Multi-Disciplinary Nature
The proposed framework showcases the multi-disciplinary nature of this research. It draws inspiration from both cognitive psychology, particularly the Common Model of Cognition, and the advancements in generative neural network models within the field of artificial intelligence. By combining these disciplines, we progress towards a more comprehensive understanding and replication of human-like intelligence.
Furthermore, the successful integration of the CMC and generative neural network models requires expertise in cognitive science, machine learning, and computer science. Researchers with diverse backgrounds can collaborate to create a truly interdisciplinary approach that shapes the future of AI.
Future Implications
The theoretical framework presented in this article opens up exciting possibilities for future research and development. By utilizing shadow productions and integrating generative network models into the Common Model of Cognition, we may achieve significant advancements in AI systems with higher-level reasoning capabilities.
Further research can explore the optimization of the shadow production systems, fine-tuning the connection between the CMC and generative neural network models for enhanced performance. Additionally, investigating the transferability of knowledge learned by the neural network models to other domains can lead to more generalizable cognitive architectures.
Conclusion
In conclusion, the adaptation of the Common Model of Cognition to incorporate generative neural network models through the use of shadow production systems presents a promising theoretical framework. This integration combines the strengths of both approaches and paves the way for AI systems with more advanced cognitive capabilities. The multi-disciplinary nature of this work emphasizes the importance of collaboration between cognitive scientists and AI researchers in shaping the future of artificial intelligence.