ARIEL: Advanced Reinforcement Incentives & Emotions based Learning
Creators: name: Jason Langhorne, Mesiah Bishop
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Artificial intelligence
subject: Artificial Intelligence
subject: Artificial Intelligence/economics
subject: Artificial Intelligence/standards
subject: Artificial Intelligence/trends
subject: Artificial Intelligence/classification
subject: Artificial Intelligence/ethics
subject: Artificial Intelligence/history
subject: Artificial Intelligence/supply &amp; distribution
subject: Artificial Intelligence/legislation &amp; jurisprudence
subject: Artificial Intelligence/statistics &amp; numerical data
subject: Machine learning
subject: Machine Learning
subject: Supervised Machine Learning
subject: Machine Learning/classification
subject: Machine Learning/ethics
subject: Machine Learning/standards
subject: Unsupervised Machine Learning
subject: Machine Learning/history
subject: Machine Learning/trends
subject: Machine Learning/economics
subject: Supervised Machine Learning/standards
subject: Unsupervised Machine Learning/classification
subject: Unsupervised Machine Learning/history
subject: Machine Learning/legislation &amp; jurisprudence
subject: Machine Learning/supply &amp; distribution
subject: Supervised Machine Learning/classification
subject: Supervised Machine Learning/economics
subject: Supervised Machine Learning/ethics
subject: Supervised Machine Learning/trends
subject: Unsupervised Machine Learning/ethics
subject: Data Science
subject: Data science
subject: Data Science/statistics &amp; numerical data
subject: Data Science/education
subject: Data Science/classification
subject: Data Science/instrumentation
subject: Data Science/methods
subject: Data Science/trends
subject: Data Science/economics
subject: Data Science/ethics
subject: Data Science/history
subject: Data Science/standards
subject: Data Science/legislation &amp; jurisprudence
subject: Data Science/organization &amp; administration
subject: Science/statistics &amp; numerical data
subject: Military Science/statistics &amp; numerical data
subject: Information Science/statistics &amp; numerical data
subject: Cognitive Science/statistics &amp; numerical data
subject: Materials Science/statistics &amp; numerical data
subject: Environmental Science/statistics &amp; numerical data
subject: Robotics
subject: Robotics
subject: Swarm robotics
subject: Robotics/economics
subject: Robotics/classification
subject: Robotics/ethics
subject: Robotics/methods
subject: Robotics/standards
subject: Robotics/trends
subject: Soft robotics
subject: Robotics/education
subject: Robotics/instrumentation
subject: Robotics/history
subject: Robotics/legislation &amp; jurisprudence
subject: Robotics/organization &amp; administration
subject: Robotics/statistics &amp; numerical data
subject: Probiotics
subject: Robotic surgery
subject: Probiotics/history
subject: Neural Networks, Computer
subject: Deep learning
subject: Deep Learning
subject: Deep Learning/history
subject: Deep Learning/classification
subject: Deep Learning/ethics
subject: Deep Learning/economics
subject: Deep Learning/standards
subject: Deep Learning/trends
subject: Deep Learning/supply &amp; distribution
subject: Deep Learning/legislation &amp; jurisprudence
subject: Deep Learning/statistics &amp; numerical data
subject: Quantum computers
subject: Quantum Theory
subject: Quantum Theory/history
subject: Quantum Dots/classification

description: This paper introduces A.R.I.E.L (Artificial Quantum Reinforcement Incentive and Emotional Learning System), a novel approach to artificial intelligence that combines quantum computing, reinforcement learning, and emotion-based decision making. We discuss its application in controlling and optimizing warp drive technology for interstellar travel, including a detailed 5-Phase Warp Sequence and Dynamic Expansion Protocol. The system's potential for revolutionizing space exploration and AI development is explored, along with its implications for future research in theoretical physics and engineering