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Cognitive architecture

What is cognitive architecture?

Cognitive architecture is a theory about the structures that create a mind in natural or artificial systems. It focuses on how these structures work with each other and use the knowledge and skills that are incorporated into the architecture to create and manage intelligent behavior in various complex environments.

The Elementary Perceiver and Memorizer (EPAM), created in 1960 by Ed Feigenbaum was one of the first possible cognitive architecture models. He intended to use the EPAM cognitive architecture model to glean insights into the inner workings of the human brain.

Generically cognitive architectures include creating artificial intelligence and modeling natural intelligence at appropriate levels of abstraction. A grand unified architecture is integrated across higher-level thought processes as well as aspects that are essential for successful intelligent behavior in human-like environments. These include emotions, motor control, and perception. Functionally elegant architectures bring an expanse of capabilities from interactions with a tiny set of mechanisms. These can be considered to be a set of cognitive Newton’s laws.

What is the purpose of cognitive architecture?

Cognitive architecture seeks to employ the research that is carried out in the domain of cognitive psychology to build a complete computer model of cognition. Cognitive architecture acts as a blueprint for creating and implementing intelligent agents. 

It concentrates on merging cognitive science and artificial intelligence and seeks to create artificial computational system processes which behave like natural cognitive systems.

The ultimate purpose of cognitive architecture is to model the human brain and eventually empower us to build artificial intelligence that is on par with humans (Artificial General Intelligence). 

Successful cognitive architecture models

In 1995, Russell S and Norvig P said that there are four ways through which artificial intelligence could be realized: systems that think like humans, systems that think rationally, systems that act like humans, and systems that act rationally.

All of these ways have been explored by the cognitive architectures currently available.

Here are three of the successful cognitive architecture models:

1. Soar

Soar’s primary goal was to create generalized agents that could perform multiple tasks and would even act as the foundation for the emulation of human cognitive capacity. Drawing from ACT-R and LIDA (which shall be highlighted subsequently), it was developed at Carnegie Mellon University by John Laird, Allen Newell and Paul Rosenbloom.


2. Active Control of Thought-Rational (ACT-R)

Active Control of Thought-Rational (ACT-R) seeks to understand how the brain organizes itself into singular processing modules, thereby minimizing cognitive functions to the most basic operations that can enable cognition. ACT-R was developed by John Robert Anderson, also at Carnegie Mellon University.


3. Learning Intelligent Distribution Agent (LIDA)

The Learning Intelligent Distribution Agent (LIDA) model was developed as an integrated model. It seeked to model human cognition all the way from perception & action to high-level reasoning. The LIDA model was developed at the University of Memphis by Stan Franklin and his colleagues.


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Cognitive architecture

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is cognitive architecture?

Cognitive architecture is a theory about the structures that create a mind in natural or artificial systems. It focuses on how these structures work with each other and use the knowledge and skills that are incorporated into the architecture to create and manage intelligent behavior in various complex environments.

The Elementary Perceiver and Memorizer (EPAM), created in 1960 by Ed Feigenbaum was one of the first possible cognitive architecture models. He intended to use the EPAM cognitive architecture model to glean insights into the inner workings of the human brain.

Generically cognitive architectures include creating artificial intelligence and modeling natural intelligence at appropriate levels of abstraction. A grand unified architecture is integrated across higher-level thought processes as well as aspects that are essential for successful intelligent behavior in human-like environments. These include emotions, motor control, and perception. Functionally elegant architectures bring an expanse of capabilities from interactions with a tiny set of mechanisms. These can be considered to be a set of cognitive Newton’s laws.

What is the purpose of cognitive architecture?

Cognitive architecture seeks to employ the research that is carried out in the domain of cognitive psychology to build a complete computer model of cognition. Cognitive architecture acts as a blueprint for creating and implementing intelligent agents. 

It concentrates on merging cognitive science and artificial intelligence and seeks to create artificial computational system processes which behave like natural cognitive systems.

The ultimate purpose of cognitive architecture is to model the human brain and eventually empower us to build artificial intelligence that is on par with humans (Artificial General Intelligence). 

Successful cognitive architecture models

In 1995, Russell S and Norvig P said that there are four ways through which artificial intelligence could be realized: systems that think like humans, systems that think rationally, systems that act like humans, and systems that act rationally.

All of these ways have been explored by the cognitive architectures currently available.

Here are three of the successful cognitive architecture models:

1. Soar

Soar’s primary goal was to create generalized agents that could perform multiple tasks and would even act as the foundation for the emulation of human cognitive capacity. Drawing from ACT-R and LIDA (which shall be highlighted subsequently), it was developed at Carnegie Mellon University by John Laird, Allen Newell and Paul Rosenbloom.


2. Active Control of Thought-Rational (ACT-R)

Active Control of Thought-Rational (ACT-R) seeks to understand how the brain organizes itself into singular processing modules, thereby minimizing cognitive functions to the most basic operations that can enable cognition. ACT-R was developed by John Robert Anderson, also at Carnegie Mellon University.


3. Learning Intelligent Distribution Agent (LIDA)

The Learning Intelligent Distribution Agent (LIDA) model was developed as an integrated model. It seeked to model human cognition all the way from perception & action to high-level reasoning. The LIDA model was developed at the University of Memphis by Stan Franklin and his colleagues.


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