Artificial Intelligence II (David Marshall)
AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems .A theme we will develop in this course note is that most AI systems can broken into: Search, Knowledge Representation and applications of the above.
Author(s): David Marshall
NA 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
Artificial Intelligence and Games
This PDF covers the following topics related to Artificial Intelligence and Games, AI Methods, Ways of Using AI in Games, Playing Games, Generating Content, Modeling Players, Game AI Panorama, Frontiers of Game AI Research.
Author(s): Georgios N. Yannakakis, Julian Togelius
359 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
