Artificial Intelligence by Seoul National University
This book explains the following topics: History of AI, Machine Evolution, Evolutionary Computation, Components of EC, Genetic Algorithms, Genetic Programming, Uninformed Search, Search Space Graphs, Depth-First Search, Breadth-First Search, Iterative Deepening, Heuristic Search, The Propositional Calculus, Resolution in the Propositional Calculus, The Predicate Calculus, Resolution in the Predicate Calculus, Reasoning with Uncertain Information, Agent Architectures.
Author(s): Seoul National University
NA Pages 
Introduction to Artificial Intelligence by Marc Toussaint
Mark Toussaint's 'Introduction to Artificial Intelligence' provides a comprehensive overview of basic and advanced AI concepts. The text delves into research design, probability theory, and the multi-armed robber problem, laying a solid foundation for understanding decision-making processes It requires Monte Carlo Tree Search and games theoretically, providing insight into the process of solving problems. The book talks about dynamic design and reinforcement learning, and shows how AI workers learn and adapt over time. Other topics include constraint satisfaction problems, graphical modeling, and dynamic simulation, highlighting various approaches to dealing with complex, interacting fields The text addresses AI and machine learning and neural network management focusing on the importance of AI presentation Both the theoretical and practical aspects of the I Provides a suitable ground.
Author(s): Marc Toussaint
248 Pages
Digital Notes on Artificial Intelligence by Sri Indu College of Engineering and Technology
Sri Indu College of Engineering Technology, Digital Notes on Artificial Intelligence provides a focused overview of basic AI concepts. The book begins with problem solving through analysis, teaching algorithms and methods for effective implementation and execution difficult problem solving. It then discusses knowledge and reasoning, discusses methods of representation, and introduces logical reasoning to support intelligent decision-making. The section on classical planning examines sequential strategies for achieving specific goals, with an emphasis on structured approaches to problem solving. Finally, comments on knowledge and learning deficits are discussed, focusing on ways to deal with incomplete or ambiguous information and options that AI systems can take to improve their performance improve over time This resource provides a clear and well-structured introduction to important AI topics, built on computer science technology It should provide a solid foundation for students and professionals.
Author(s): Department of CSE, Sri Indu College of Engineering and Technology
140 Pages
Introduction to Artificial Intelligence by Thomas P Trappenberg
Introduction to Artificial Intelligence by Thomas P. Trappenberg provides a comprehensive insight into AI concepts, presented by Dalhousie University. The essay begins with an introduction and history, providing a foundation for the development of AI , It includes research designs and their applications, followed by Robotics and Motion Planning, which are robotic While exploring the integration of AI in the design, the paper delves into constraint satisfaction problems, dealing with solution methods handles complex constraints, including learning machines and perceptrons, and improves with regression, classification , and maximum likelihood techniques support vector machines, learning theory, and naive Bayes and other generative models, probabilistic reasoning is also available, including Bayesian networks and Markov models Overview of essential AI methods and applications.
Author(s): Thomas P Trappenberg, Dalhousie University
208 Pages
Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
Dr. A.S. The PDF of Prashant Kumar's paper titled Lecture Notes On Artificial Intelligence provides an in-depth analysis of the basic concepts of AI. It begins with AI Techniques which introduce the techniques used in artificial intelligence. The notes include Level of the Model, detailing the various levels of abstraction in AI systems. Problem space and search include problem definition as a state space search and associated methods. Processes are analyzed, including their problem characteristics and product characteristics. The book addresses research design issues and presents heuristic search methods such as generate-and-test, hill climbing, best-first search, problem-reduction, constraint satisfaction, and means-end analysis in this Symbolic Reasoning Under Uncertainty and Game Playing this outcome was also discussed, showing the role of AI in strategic decision making. Finally, learning: learning by imagination is discussed, focusing on the fundamentals of how AI systems acquire knowledge through repetition.
Author(s): Dr. Prashanta Kumar Patra
128 Pages
