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
Digital notes on Artificial Intelligence
Department of Information Technology's 'Digital Notes on Artificial Intelligence' at Malla Reddy College of Engineering and Technology provides a comprehensive overview of AI concepts. It begins with an introduction to AI, setting up more advanced topics . The essays include a variety of search methods, including A Search, and overcome challenges such as partial discovery searches. Techniques such as Alpha-Beta Pruning have been introduced in order to optimize the search process. The text explores ways of understanding including forward and backward chains, and delves into the syntax and semantics of first-order meaning. This includes all knowledge technologies, decision-making, and classical planning including state space exploration. In addition, it involves practice in random environments, multidimensional design, probabilistic reasoning using Bayes rules, Bayesian networks, Dempster-Shafer theorem The presentation goes on to say useful things on such as learning decision trees and the importance of knowledge in curriculum design.
Author(s): Department of Information Technology , Malla Reddy College Of Engineering and Technology
143 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
