Cognitive Architecture is a blueprint for intelligent agents. It represents the underlying structure of intelligent systems including human consciousness, incorporating aspects of cognition like memory, reasoning, and perception.


Think of Cognitive Architecture like the brain of a robot. Just like our brain allows us to think, remember stuff, and understand things, cognitive architecture allows machines to do the same.

In-depth explanation

Cognitive Architecture is a complex, comprehensive system that structures the implementation and design of intelligent agents. This design incorporates both architectural components, which serve as a foundation for the system’s capabilities, as well as algorithms and mechanisms that actually carry out the higher-order cognitive processes.

Taking inspiration from human cognition, these architectures guide the imitation of human thought processes in machines. This involves incorporating elements of perception, learning, language comprehension, problem-solving, reasoning, and even consciousness. The end goal is to create systems that can understand, learn, and react to complex real-world scenarios.

Cognitive architectures can be broadly classified into two categories. Symbolic architectures, like SOAR and ACT-R, use symbols to represent knowledge, and operations on these symbols constitute cognition. On the other hand, connectionist or sub-symbolic architectures, like Neural Networks, perform cognition through the alteration of connection weights between nodes.

Cognitive architectures also incorporate various types of memory, including short-term and long-term memory, enabling the machines to learn from past experiences. The control structure determines when and how the different modules activate and interact. For example, in rule-based systems, production rules determine which action to take under what circumstances.

These architectures not only provide a theoretical foundation to understand intelligence but also deliver practical constructs for constructing intelligent systems. However, despite progress, there remains a substantial gap between the complexity of human cognition and the capabilities of current cognitive architecture, a challenge that drives continued research and development in the field.

Artificial Intelligence, Machine Learning (ML),, Neural Networks, Symbolic AI, Computational Intelligence, Cognitive Computing, ACT-R, SOAR, Intelligent Agents, Cognitive Models, Connectionist Models