Neural Networks With Disabilities: An Introduction to Complementary Artificial Intelligence

Area of Science:

• Cognitive Science
• Artificial Intelligence
• Machine Learning

Background:

• Machine learning models map perceived information to actions, simulating human cognitive skills for optimal behavior in agents.
• Autonomous systems rely on sensors for perception and actuators for reactions, with cognitive models often implemented as neural networks.

Purpose of the Study:

• To train machine learning models to simulate cognitive disabilities.
• To explore the
• coolability
• paradox, where disabilities may enhance other capabilities.
• To improve autonomous system efficiency in adversarial conditions through pretrained simulated disabilities.

Main Methods:

• Adaptation of several neural network architectures.
• Simulation of cognitive disabilities by modeling the absence of cognitive sensors or actuators.
• Training cognitive models with these simulated disabilities.

Main Results:

• Demonstration of neural network architectures adapted to simulate cognitive disabilities.
• Conceptual framework for
• coolabilities
• as complementary artificial intelligence.
• Potential for enhanced autonomous system performance in challenging environments.

Conclusions:

• Training autonomous systems with simulated cognitive disabilities can lead to improved performance.
• The concept of
• coolability
• offers a novel approach to AI development.
• This strategy holds promise for diverse applications requiring robust autonomous agents.