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 
Artificial Intelligence Foundations of Computational Agents
This book contains below topics, What is artificial intelligence, Agent architectures and search, Reasoning with constraints, Propositions and inference, Planning, Supervised machine learning, Neural networks and deep learning, Reasoning and planning under uncertainty, Multiagent systems, Reinforcement learning.
Author(s): David L. Poole and Alan K. Mackworth, University of British Columbia
904 Pages
Lecture note on Artificial Intelligence
This note describes the following topics, introduction to AI and production systems, Representation of Knowledge, Knowledge Representation using predicate logic, Knowledge Inference, Planning and Machine Learning, Expert Systems and Meta Knowledge.
Author(s): St Anne College of Engineering and Technology
173 Pages
Introduction to Artificial Intelligence by Thomas P Trappenberg
This note covers introduction and history, Search, Robotics and motion planning, Constraint satisfaction problem, Machine learning, Learning machines and the perceptron, Regression, classification and maximum likelihood, Support vector machines, Learning Theory, Generative models and Na and iuml,ve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.
Author(s): Thomas P Trappenberg, Dalhousie University
208 Pages
Lecture Notes On Artificial Intelligence By Dr Prashanta Kumar Patra
This book covers the following topics, AI Technique, Level of the Model,Problem Spaces, and Search, Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques, GenerateandTest, Hill Climbing, Bestfirst Search, Problem Reduction, Constraint Satisfaction, Meansends, Symbolic Reasoning Under Uncertainty, Game Playing, Learning, Rote Learning.
Author(s): Dr. Prashanta Kumar Patra
128 Pages
