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AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal.
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English: Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig "This is an open-source repository for the book Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig (1992), and the code contained therein.
Hassan Khosravi and Bahareh Bina: A Survey on Statistical Relational Learning, Advances in Artificial Intelligence, Lecture Notes in Computer Science, Volume 6085/2010, 256–268, Springer, 2010 Ryan A. Rossi, Luke K. McDowell, David W. Aha, and Jennifer Neville : Transforming Graph Data for Statistical Relational Learning , Journal of ...
Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Knowledge representation and reasoning (KRR, KR&R, or KR²) is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog.
A Wikipedia page providing an overview of the Lecture Notes in Computer Science series, including its history and content.
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