AI reasoning explores how machines use information, knowledge, evidence, and context to derive conclusions, make decisions, plan tasks, and take actions. The book traces the evolution from rule-based systems and knowledge representation to machine learning, deep learning, Transformers, and large language models. It explains deductive, inductive, abductive, and probabilistic reasoning, along with planning, task decomposition, tool use, memory, and agentic AI. It also examines hallucination, bias, reliability, security, explainability, and responsible AI. Real-world applications include healthcare, education, finance, robotics, software engineering, cybersecurity, and everyday assistants. The book concludes with future directions involving hybrid AI, autonomous agents, and human-AI collaboration.
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