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Fouad Sabry

Learning Intelligent Distribution Agent

What Is Learning Intelligent Distribution Agent

The LIDA cognitive architecture is an integrated artificial cognitive system that seeks to simulate a broad spectrum of cognition found in biological systems, ranging from low-level perception/action to high-level reasoning. It does this by using a combination of natural and artificial cognitive processes. The LIDA architecture is experimentally anchored in cognitive science and cognitive neuroscience, and was developed principally by Stan Franklin and colleagues at the University of Memphis. In addition to generating hypotheses that can direct subsequent research, the architecture can also provide support for control structures that can be used by software agents and robots. The LIDA conceptual model is not only meant to be used as a tool for the purpose of thinking about how brains operate, but it also provides credible explanations for a large number of cognitive processes.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: LIDA (cognitive architecture)

Chapter 2: List of artificial intelligence projects

Chapter 3: Cognitive science

Chapter 4: Artificial consciousness

Chapter 5: Cognitive model

Chapter 6: Soar (cognitive architecture)

Chapter 7: Stan Franklin

Chapter 8: Global workspace theory

Chapter 9: Cognitive architecture

Chapter 10: Computational theory of mind

(II) Answering the public top questions about learning intelligent distribution agent.

(III) Real world examples for the usage of learning intelligent distribution agent in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of learning intelligent distribution agent' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of learning intelligent distribution agent.
110 printed pages
Original publication
2023
Publication year
2023
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