Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
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: Generate-andTest, Hill Climbing, Best-first Search, Problem
Reduction, Constraint Satisfaction, Means-ends, Symbolic Reasoning Under
Uncertainty, Game Playing, Learning: Rote Learning.
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
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 Naïve Bayes,
Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian
networks and Markov models.
Author(s): Thomas P Trappenberg,
Dalhousie University
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
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.
This note provides a
general introduction to artificial intelligence and its techniques. Topics
covered includes: Biological Intelligence and Neural Networks, Building
Intelligent Agents, Semantic Networks, Production Systems, Uninformed Search,
Expert Systems, Machine Learning, Limitations and Misconceptions of AI.
This note is
designed as a broad rather than in-depth introduction to the principles of
artificial intelligence, its characteristics, major techniques, and important
sub-fields and applications.
This note covers the following topics: Search, Backtracking
Search, Game Tree Search, Reasoning Under Uncertainty, Planning, Decision Making
under Uncertainty.
This book is
based on the EC (ESPRIT) project StatLog which compare and evaluated a range of
classification techniques, with an assessment of their merits, disadvantages and
range of application. It provides a concise introduction to each method, and
reviews comparative trials in large-scale commercial and industrial problems.
Author(s): D. Michie, D.J. Spiegelhalter, C.C. Taylor